CA1229921A - Speech recognition method having noise immunity - Google Patents

Abstract

Abstract of the Disclosure A speech recognition method and apparatus employ a speech processing circuitry for repetitively deriving from a speech input, at a frame repetition rate, a plurality of acoustic parameters. The acoustic parameters represent the speech input signal for a frame time. A plurality of template matching and cost processing circuitries are connected to a system bus, along with the speech processing circuitry, for determining, or identifying, the speech units in the input speech, by comparing the acoustic parameters with stored template patterns. The apparatus can be expanded by adding more template matching and cost processing circuitry to the bus thereby increasing the speech recognition capacity of the apparatus. The template matching and cost processing circuitries provide distributed processing, on demand, of the acoustic parameters for generating through a dynamic programming technique the recognition decision. The apparatus employs a method for inhibiting a response to nonvocabulary utterances to effectively provide improved noise immunity.

Description

~2299~L

SPEECH RECOGNITION METHOD
HAVING NO IS IMMUNITY

;: ' ack~round of the Invention :: The invention relates generally to the field of speech recognition and in particular to the recog-notion of speech elements in continuous speech Jo The need for speech recognition equipment which is reliable and reasonably priced lo well dock-minted in: the technical literature:. Speech recognition equipment generally falls in two main categories. One category is speaker independent equipment wherein the speech recognition apparatus is designed to recognize elements of speech from any person However speaker independent systems can be quite limited with regard : to features other than the "speaker independence", for example, the number of words irk the recognition vocal-ulary. Also, typically, five to ten percent of the Jo population will not be recognized my such systems. The i: other category speaker dependent speech recognition, relates to speech recognition apparatus which are tub-.

staunchly trained to recognize speech element of a :: , ' ~2~39;~

limited class, and in particular the class consisting of one person. Within each category, the speech fee-ignition apparatus can be directed to the recognition of either continuous speech, that is, speech wherein the boundaries of the speech elements are not defined, or to isolated speech, that is, speech in which the boundaries of the speech elements are à priori known.
An important difference between continuous and is-fated speech recognition is that in continuous speech the equipment must make complex "decisions' regarding the beginnings and ends of the speech elements being received. For isolated speech, as noted above, the incoming audio signal is isolated or bounded by either a given protocol or nether external means which makes the boundary decisions relatively simple.

There exist today many commercial systems for recognizing speech. These systems operate in either a speaker independent environment (as example-fled for example by USE Patents 4,038,503; 4,227,176;
4,228,498; and 4,241,329 assigned to the assignee of this invention) or in the speaker dependent environ-mint. In addition, the commercially available equip-mint variously operate in either an isolated speech or a continuous speech environment.

The commercially available equipment, how-ever, is expensive when high recognition performance is required This it often a result of the best equipment being built for the most difficult problem, that is, speaker independent, continuous speech recog-Nina. Consequently, many of the otherwise available applications to which speech recognition equipment could be adapted have not been considered because of the price/performance relationship of the equipment.

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Furthermore, the commercially available equipment can-not easily be expanded to provide added capability at a later date, and/or does not have the required awoke-racy or speed when operating outside of the laboratory environment Primary objects of the present invention are therefore an accurate, reliable, reasonably priced continuous speech recognition method and apparatus which can operate outside of the laboratory environment and which enable the user to quickly and easily establish an operating relationship therewith Other objects of the invention are a method and apparatus generally directed to speaker dependent, continuous speech recognition, and which have a low false alarm rate, high structural uniformity, easy training to a speaker, and real time operation.

Summary of the Invention The invention relates to a speech recognition apparatus and method in which speech units, for exam-~2:0 pie words, are characterized by a sequence of template patterns. The speech apparatus includes circuitry for processing a speech input signal for repetitively de-roving therefrom, at a frame repetition rate, a plus : reality of speech recognition acoustic parameters. The :25 acoustic parameters thus represent the speech input signal for a frame time. The apparatus further has circuitry responsive to the acoustic parameters or generating likelihood costs between the acoustic par-emoters and the speech template patterns. The circuitry is further adapted for processing the like-Lydia costs for determining or recognizing the speech units of the speech input signal.

I l The invention features a method for inhibiting a response by the apparatus to non-vocabulary utterances in the speech input. The method features generating likelihood costs at each S frame time for the acoustic parameters and template patterns, the template patterns including a pattern representing silence If the cost for an active template pattern or acoustic kernel is better Han a predetermined threshold value the system begins a I normal speech recognition process. If the cost of the active template patterns, including the silence template pattern, are all worse than the predetermined threshold value the system reverts to a non speech recognition process. If the silence template corresponds to the only likelihood cost which is better than the threshold value then the system remains in a quiescent state.

Further in accordance with the method, a second threshold value can be set and the apparatus will remain in the non-speech recognition process until a likelihood cost of the silence template is better than the second threshold. In specific embodiments, the two thresholds can be equal and various silence patterns can be selected to represent short and long silence.

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Brief Description of the Drug Figure 1 is a schematic block diagram showing an overview of the speech recognitiorJ apparatus;

inure 2 is a schematic flow diagram of the 5: signal processing portion of the illustrated embody-mint of the invention;

Figure 3 it a detailed slow diagram of the dynamic programming and template snatching portion according to the invention;

.10 Figure 4 is a more detailed schematic block diagram of he uniform processing circuitry portion according to the invention;

Figure 5 it a schematic representation of a dynamic progra~Nning grammar graph according to the invention;

Figure 6 is a schematic representation of a dynamic programming word graph according to the invention;

: Figure 7 it a schematic representation of a grammar using a single element joke word according to : the invention;

Figure B is a schematic representation of a grammar using a double joker word according to the inventions Figure pa is a schematic representation of a - grammar using a double joker word as a prowled to speech recognition;

Figure 9 is a diagrammatic block diagram of a prior art apparatus; and Figure 10 is a diagrammatic lattice presentation of the dynamic programming approach.

D i tion-of a Preferred Embodiment esquire P
An Overview Referring to Figure 1, a speech recognition apparatus 10 according to the invention receive an audio input over a line 12~ The audio input is buff freed and filtered by a preamplifier 14. The prearm-plifier 14 has an anti-alia~ing jilter and provides an lo output voltage over a line 16 which ha the proper voltage values (that is, is normalized) for an analog-to-digital converter 18~ In the illustrated embody-mint, the analog-to-digital converter operates at a rate of 16,900, twelve bit conversions per second and provides the twelve bit output on lines 20. The twelve bit output of the analog-to-digital converter is directed to a buffer memory 22. Tube buffer 22 has a capacity for storing up to 320 samples or twenty milliseconds of speech. Ire minimum requirement is that buffer 22 be able to store slightly more than 160 samples.

The buffer I is connected to an internal data bus 24. The bus 24 acts as the primary data come monkeyshines bus far the apparatus. The bus 24 thus -~L2~9~

interconnects buffer 22 with a signal processing air-quoter I a f first template matching and Dynamic pro -tramming circuitry 28, a second template ~at~hinq and dynamic programming circuitry 30, a process con-trolling circuitry 32~, an acoustic parameter buffer memory 34, and a trace back buffer 36~, The recognition prowesses is controlled by the process collateral circuitry 32 which incorporates, for - example, a commercial microprocessor as the control element. The process control circuitry uses the memory 34 to enable the apparatus to operate with a variate processing rate. This means that the entire apparatus need not meet peak load demands in real time but only needs to operate in real time with respect to the average processing load. the hardware savings involved by employing this process configuration are substantial and are discussed in greater detail below.

Each of the circuitries 26, 28, and 30, in the illustrate embodiment, are structurally identical.
: 20 They aye modified by the Satyr programs placed therein to per~crm either the audio signal processing of circuitry 26 or the template matching and dynamic programming of circuitries I and 30. This is discussed in more detail below. Each of the circuit-ryes 26~ 28, and 30 have therein a small 2,000 word memory aye, aye, and aye respectively sixteen bit works. These memories act as small fast" buffers providing sufficient storage for continuous processing in thy sorters 26, 28, and 30. Ike apparatus 10 rocketry can be easily expanded along bus 24 by adding additional template matching and dynamic processing \ I``` ) I

circuitries such as circuitries 28 or 30~ to process a larger no ignition vocabulary. This expansion gape ability is a direct result ox the chosen architecture which dictates that template matching and dynamic pro-sousing be executed on the same machine board and in this particular embodiment by the use of identical circuitries or circuitries 28 and 30.

In the illustrated embodiment r each of the sequiturs 26, pa, and 30 occupy one complete board of the assembly. While it would have been desirable to combine two ox the data processing boards, the pry-steal size of the combined board would not fit into the rewaken, and hence in today's semiconductor tech-neology, it way not feasible to combine the boards.
:15 The structure which has been developed, however, not only allows new boards to be added along bus 24 as de-scribed above, but reduces the data communications "log jay" that typically occurs in prior apparatus using separate template matching and dynamic pro-ZOO tramming circuitries (see for example Figure g which shows a signal processing circuit 46 feeding a them-plate watching circuit 48 and the template matching circuit 48 feeding a dynamic programming circuit 50).
As a result of using separate template matching and dynamic programming circuitries, bandwidth problems invariably occur at the intrinsically high bandwidth connection 52 and must be solved. In the illustrated embodiment of the invention the apparatus structure Lowe for parallel processing on demand as described below, thereby reducing the bandwidth retirement along the bust correspondingly decreasing the cost of the apparatus, ~2;~99;;~

g The speech recognition apparatus and method implemented by the illustrated embodiment processes the audio input over line 12, in the signal processing circuitry 26, to provide a sex of acoustic parameters characterizing the input speech. It is these pane-meters which are stored in memory 34. The set of acoustic parameters can be thought of, in the thus-treated embodiment, as a vector of sixteen eight-bit numbers or components. A new vector of acoustic pane-meters is generated each frame time, a frame time in the illustrated embodiment being ten milliseconds.

The acoustic parameters are called for, on demand, by the first and second template matching and dynamic programming circuitries. These circuitries, in :15 general operation, compare each vector with prescored reference template patterns, only as needed, and gene-rate likelihood statistics or C05tS representative of the degree ox similarity. the previously stored rev-erroneous template patterns characterize the speech eye-~20 mints to be recognized using the same processing methods employed in generating the acoustic pane-meters. Each speech elements, for example a word, is characterized by a sequence of template patterns as described in more detail below. A dynamic programming method is e~ployea to generate a recognition decision, based upon the likelihood statistics or costs, in a manner which allow the apparatus to operate in real iamb The description below first descries how the acoustic parameters are generated. It then details the processing of the acoustic parameters, by air-`

~2992~L i quoters 28 and 30, to obtain a recognition decision.
It is important to note and appreciate that circuit-ryes 2Q and 30 are more than identical in structure;
they operate in parallel in order to distribute the processing load and further enable real time operation.

Audio Signal Processing Referring now to figure 2, an acoustical input 100 over line 12 of Fly. 1) is passed at 101 to an A/D converter (18 in Fig. 1) after the necessary normalization and buffering (sown in Figure 1 by pro-amplifier 14). The output of the A/D converter, after buffering by memory 22 (Fig. 1) is processed by the signal processing circuitry 26 as follows.

In the signal processing description that follows, the processed value of the data will often be clipped or normalized so that they fit into one eight bit byte. This is done so that a subsequent multiplication and/or accumulation does not produce any overflow beyond sixteen bits, and with respect to normalization to make best use of the available dynamo to range.

The twelve bit output of the A/D converter is differentiated and clipped at 1020 the input is differentiated by taking toe negative first differences between ~accessive input values. This occurs at the 16 I sampling rate, The differencing procedure no-dupes the dynamic range of the input waveform and pro-emphasizes the high frequency. In the frequency domain, the effect of differentiation is multiplica-lion by frequency which results in a six dub per octave ~99~

"boost for the high frequencies. This high frequency reemphasis is desirable because the amplitude of the speech signal decreases as a function of frequency.
The differentiated acoustical signal is then clipped so that it fits into one byte. I
I

The average amplitude, the mean, and the logo the mean squared amplitude ox the differentiated and clipped output is then determined at 10~, 105 for, in the illustrated embodiment, a "window" having 3~0 I sample or twenty milliseconds ox speech. The log used here is:
8 log (amplitude) - 128 eke. 1) The result is then clipped to fit into a single byte.

While the window" used here is twenty mill liseconds in duration, it is important to recognizethatO according to the invention, the signal processing circuitry is intended to generate a new set of acoustic parameters (as described below) every ten milliseconds.
Successive windows therefore overlap by ten Millie second it the illustrated embodiment exit, the differentiated and clipped data oft twenty millisecond window is normalized at 106 by subtracting therefrom the mean amplitude over the Wendy. This it in effect equivalent to subtracting :25 thy zero frequency component, or I level, of the sign net. The normalized data is clipped again to fit within a single byte.
-The normalized output from block 106 is then "windowed" at aye. Winnowing is multiplication of the input array by a vector of window constants at 109 which has the of foot of attenuating the data at both :5 ends of the window. In the frequency Dylan, the height of the side lobes is thereby seduced at the cost of increasing the width of the center lobe thus smoothing the resulting spectral estimate. While various types of windowing functions exist, each producing slightly different tradeoffs between side lobe height and center lobe width, the window chosen it the illustrated embodiment and which has been found to produce statistically better results is swanker window which consists of multiplying the :15 sine function (sin Ajax by a Raiser function.

The Kaiser window is useful since it pane-motorizes the trade-off between side love height and center lobe width. Multiplying by the sine function gives a bandwidth at each frequency in the Fourier transform. In the illustrated embodiment, a bandwidth of 355 hertz is used In the Kaiser function window, the beta parameter, I, is set at I

I

The raiser function, which is described for example in digital Filters, chapter 7 of System nal~sis by Dig Italy Computer, Duo arid ashore, John Wiley & Sons, New York, 1966, is given by eke. 2) It (13 (n/ (N-l) Jo 2 (rut/ ( No I ) ) 1/2 ) X (n) It (B ) where It is a motif ted zeroth order Bessel function of the first kind:

or eke. pa) It I = Jo I, gosh (Z c08 do .
The 5 i no f unyoke t ion f r the pa r emote r s of the illustrated embodiment is: .

(En. 3) a I- ~N-1)/2) (n = 0,1,~. . No and N - 320 points in the window where a = 2 If ( 355J2) - . û697 The waveform is windowed a ton normalization (rather than before normalization since otherwise the mean would introduce an extra rectangular signal which would increase the side lobes. The windowed waveform is block normalized so that its samples fit in thin-teen bits. This I so that accumulation performed during folding as described below, does Seiko overflow si:Kt~n bits.
:
The discrete Fourier transform of the win-dozed date i now performed at 11~ . While there are many ways off efficiently performing a Fourier trays-form, in order to reduce the number ox multiplications an hence the tome to effect the Fourier transform ( ) computation, a folding technique a 113 making use of the symmetries of the sine and cosine, by converting the data vector into four smaller vectors, is performed. Since the sampling of values in the frequency domain is done at multiples of a base frequency, each of the resulting vectors will hove a length of 1/4 the period of the base or fundamental frequency, also called the base period.

In the illustrated embodiment, the base ore-unwise from a 16 kilohertz sampling rate and a 20 mill lisecond window is chosen to be 125 hertz. (The eon-responding base period is 128 samples.) This repro-sent the minimum chosen spacing between spectral frequency samples. As noted above, a 20 millisecond window encompasses two 10 millisecond frames of the incoming acoustic Ann digitized) signal. Successive windows" thus have an overlapping character and each sample contributes in two windows.

According to tube folding technique, frequent ales are divided into odd multiples and even multiples of the base frequency The real (multiplication by the cosine) and the imaginary (multiplication by the sine) components of the transform will each use one of the resulting folded vectors for both classes ox frequency.

The folding operation is performed in three steps. First, the element which are of fret by the base period are grouped together. In the illustrated embodiment, using a base frequency of 125 hertz at a sampling rate of 16 kilohertz, this results in 128 ) I., .

go .

points. This "fold" is a direct consequerlce of the expression for the Fourier transform F(nf) - I eye jnkf) (En. 4) where f is the base frequency ~125 ho awns the sum is extended from k = O through the number of the samples irk the window less 1). Since eye) = e joy + 27r ), (Erg, 5 the transformation can be rewritten as:
Enough) k Al et-2Tr jnfk) (En. 6) :10 where the sum is extended from k = O through k = 45j-1 and is equal to 1/4 ox the base period and Al is the sum of I x(k+4q) + x~k+8q) the second fold operation is performed by rewriting the vast expression (Equation 6) as:
lo Pine) k ilk lcosl2~r nfk~ j Sweeney nfk~).

ode earl then use the symmetries ox the sine and cosine functions since sin (a) = -sin (try a) and coy (a) -Shea try a), the tranfQrm of Equation 7 can be rewritten as:

::~20 F(nf~=~x2c kiwi nfk) + issue Sonora nil (En. 8) where x2c Al (k) Al (cluck) and x2~ = Al I - Al I k).
Thy sum extends Rome k = 0 to k = I Thus there ~25: are 64 terms where the sampling rate is 16 kilohertz and the base frequency it 125 Horace.

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The third stew of the procedure resolves the instance of odd multiples of the base frequency. The symmetries sin = Sweeney) and Casey = -Casey) allow the transformation of Equation 8 to be rewritten as:

F(nf~ = kx3Co Casey nfk) + j X3so Sweeney nfk) (En. 9) where X3co = X2 coy - X2 Cossack - lo and X3so = I sink) x2 Sweeney - l-k).
For even multiples of the base frequency the equations are: .

F~nf) = kX3CE Casey nfk) + j x3SE Sweeney nfk) let. 10) where x3cE = x2 coy + I Casey - lo and x3sE = x2 sink) - x2 Sweeney k).
This procedure uses the equalities of sin =
-Sweeney) and coy - Casey). The sums, after the third procedure or third fold, now extend from Nero to k Q-l. That is 32 terms for a sampling rate of 16 kilohertz and a base frequency of 125 hertz. After the three folds, the vectors are block normalized to six bits.

At this point the discrete Fourier transform is completed by multiplying the date from the folding procedure by a matrix of sines and cosines (aye). my calculating the multiple of the base frequency, - ) module 2, the set of folds which are nodded to be used can be determined. The resulting vectors are block normalized to fit within a single byte. The result of the Fourier analysis is two vectors of signed integers, the integers ranging from -128 to 127 that is, one byte), one of the vectors containing the real term of thy Fourier analysis and the other containing the imaginary terms of the analysis. The length of the vectors is equal to the number of frequencies employed I during the recognition process. In the illustrated embodiment the number of frequencies is thirty-one and are:

~50 1250 2250 3250 SO loo 25~ 3750 7~0 1750 Z750 SEIKO
87S 1~75 2875 5000 1000 20~0 ~000 5500 20 1125 2125 31~S

The next step, at 114, is to determine the sum of the square of the real and imaginary parts of the spectrum at each multiple of the vase frequency.
The result is divided by two, that is, shifted down Z5 one bit so that the square root which is computed at 116 will fit into one byte.

After thy square root is taken at 116, the no ulting spectrum it transformed, at 118, according to the equation:

l - 128(x - monks Jean). I 11) .. . . .. ..... . ..

1 Tunis function, often called the "quasi-log" and described in more detail in US. Patent No. 4,038,50$, enhances the speech recognition properties of the data by redistributing the dynamic range of the signal. The "meant' is the average value of the array of spectral values.
The result of the quasi log is a vector having, in the illustrated embodiment, thirty-one acoustical parameters. Added to this vector r at 120, is the mean amplitude value of the input signal calculated at 104 and 105. This amplitude value and the 31 element vector resulting from the quasi-log step at 118 are applied to a principal component transformation at. 1220 The principal component transformation reduces the number of elements in the vector array to provide a smaller number of acoustical parameters to compare against the speech representing reference template patterns prestGred in the apparatus.
This reduces both computational cost and memory cost.
The principal component transformation, at 122, is performed in three part. In the first step a speaker independent mean for each element of the vector is subtracted from the respective element in the vector.
Second, the result of the first step is multiplied by a speaker independent matrix which is formed as described below. Finally, each element of the resulting vector from the second step is individually shifted by an element dependent amount and then is clipped to fit within an eight bit byte (see 123). This essentially means that the distribution of each component of the vector has been normalized so that ~22~

the byte will contain three standard deviations around the mean of the component. The output of the principal component analysis, an array ox bytes having a vector length equal to a selected number of components, is the acoustic parameter output of the signal processing section 26 of the apparatus of Figure 1 and is applied to the bus 24 for further processing as described In more detail below The principal component analysis is employed to reduce the number of acoustic parameters used in the pattern recognition which follows. Principal come potent analysis it a common element of many pattern recognition systems and is described in terms of an eigenvector analysis for example in ITS Patent No.
4,227,177p assigned to the assignee of this inn Zion. The concept of the analysis is to generate a et of principal components which are a normalized linear combination of the original parameters and where the zecombination is effected to provide come pennants with maximum information while maintaining independence from each other. Thus, typically, the first principal component is the normalized linear combination of parameter with the highest variance The concept is that the direction containing the most variation also contains the most information as to which class of speech vector belongs to Normally, the vectors containing the linear combinations under consideration are constrained to be of unit length. This would be the best procedure if all recombined parameters had the same variation with-in the defined speech classes, but unfortunately they do not. rho prior art anal is method is therefore i .: ) Shea -20~

modified to normalize all linear combinations of pane-meters to have the same average, within class, van-ante and the first principal component is chosen to be that normalized linear combination with the highest across class variance.

the principal component analysis is derived as follow. Let M' represent the trounces of a matrix M. Let T be the caverns matrix of some set of P
original parameters. Let W be the average within class caverns matrix. Let V be a P dimensional vector of coefficients, and let X be a P dimensional vector of acoustic parameters. For simplicity ox derivation, assume that the original parameter vector X has a zero mean. In practice, in the illustrated embodiment the assumption is implemented by subtracting from the oft-Gina parameters, their respective mean values The : variance of a linear combination of parameters VEX
is I 12) Expectation of [~V'X)~V'X)'] - V'(XX')V = TV

I since I is defined as the expectation of XX'. Sue-laxly, if Wit) is the caverns of the ilk class of parameters, then Viva is the caverns of the parameter VEX in the ilk class. The average within class variance is then I Viva eke. 13) i31 where N is the number of classes. By the distributive property of matrix multiplication, this is just equal to VIVA. this enchain weights ail classes equally, independent of the number of samples contained in each class. Actually, in the illustrated embodiment, all samples were weighted equally on the assumption that it is more important to discriminate between classes that occur frequently.

Next TV is maximized under tube constraint: VIVA = 1 (En. 14 This problem can be solved by using Lagrange multi-plower Let the one Lagrange multiplier be denoted by 10 y. We then want to solve eke. 14) and (En. 17~ (below) f TV - y TV - 1) let. 15) O - df/dV 5 TV - 2yWV (En. 16) TV - yWV eke. 17) .
This equation 117) is just the general eigenvector problem and is solved by P eigenvectors with real eigenva}ues, due to the symmetry of T and W.

Thy solution which maximizes the original criteria is given by the ei~envector V with the largest eige~value y. This can be seen by observing that the :20 variance of VEX it given by TV = Viva - yV'WV - y 1 = Y (En. 18) Let the solution to (En. 173 wit the largest ei~envalue be Ye, and the corresponding eigenvalue be Ye- or the next parameter, solve for the linear combination of parameters VEX which maximizes lV'TV), which has unity within class variance, lV'WV - 1), and which is uncorrelated with Vl7X. The condition that ~2;2~32~

VEX is uncorrelated with Vex allows the analyst s to find the linear combination of parameters which has maximum across-c7ass variance compared to Within class variance, but which does not include information already available from the first component. By the definition of uncorrelated, we have (En. 19) Expectation of lox (Vl'X)'l = V'(XX')Vl = V'TVl = 0 Let y and z be Lagrange multipliers, and solve (Equal-lions 14, 19 and 21):

g = TV ytVi~v z(2V'TVl) (En. Jo) O Y dg/dV TV - 2yWV - 2zTVl (En. 21) multiplying through on the left by Al and dividing by 2:
Al TV yVl TV - zVl~lVl (En. 22) Substituting TV z 0 as a constraint; and Wylie =
T~l/yl from eke. lay in these relations; and mull toppling through by -1:
twill) Al TRY) zylVl~WVl (En. 23) 0 = Swahili) Zulu o (En. 24) z - 0 (En. 25) Therefore, given Eli toe following three equations can be solved:
TV yWV eke. 26) VIVA - 1 (En. 27) V'*Vl (En. 28) Any vector which satisfies (En. 26) can be turned ionic a vector which satisfy its botch (En. 26 and 27) by dividing Q by the sealer Q 'WE.

Consider any two vectors, Q and R, satisfying I 20, with corresponding eigenvalves q and r. when ~;~2992~

rQ'WR = TRY - RUT = qR'~Q - qguwR (En. 29~
trucker - ox eke. 30) If r is not equal to then 0 R = truer eke. 31) TRY = I eke. 323 It (En. 26) is satisfied by two vectors with different nonzeros eige~values, then those two vectors Allah sat-iffy I 28), and are therefore uncorrelated.

The second companion is thus chosen to be the vector V2 which satisfies (En. 26 and 27) and which has the second largest eigenvalue, assuming the two largest are distinct. Correspondingly, the nth come potent Van can be made uncorrelated with Ye through Vn_l and the same procedure can be employed to show that Van must only satisfy (En. 26 and 27), so long as there are n distinct nonzeros eigenvalues.

In this manner, assuming that the character-icky polynomial corresponding to let. I has N disk tint nonzeros roots, a sequence of N linear combine-lions of parameters, each one maximizing the ratio VET VIVA), while constrained to be unco~related with the previous linear combinations in the sequence, con be determined. This sequence comprises the N gent realized principal components.

In the method described above the linear combinations of parameters that maximize TV while holding VIVA constant are found. Since, as can be easily shown, T - W B, By is the "average between-ala s variance, the same results can be obtained by maximizing V'BV while holding VIVA constant Since B
can be expressed as the em of terms involving dip-~L~2g92~

furnaces between pattern means, intui~ive].y, by maxim mixing V'BV, the pattern means are being moved apart from each other. It may be that in speech recognition it is more important to distinguish between some pat-terns than between others, If the differences between pattern jeans in the formulation of B are weighted by different amounts, ho resultant principal components will be biased to differentiate more between some pat-terns than others. One instance for assigning dip-fervent weights to different pattern pairs is when data and patterns from different speakers are used in the principal components analysis. In such a case it is not worthwhile to try to separate patterns from dip-fervent speakers, and all such pattern pairs should .15 receive the weight of zero.

The creation ox the principle component ma-trip takes place prior to the real time speech recog-notion process. Typically, a large data base is : require to obtain a good estimate of the matrix.

:20 The output of the principal component trays-formation is the acoustic parameter vector. A new "vector" or set of acoustic parameters is made avail-able each frame time, that is each ten milliseconds in the illustrated embodiment. The acoustic parade-lens are available on bus 24 from the signal process-in circuitry 26. Since each frame time has a window repro ending twenty milliseconds of input audio, there is as noted above, an overlap in the information rep-resented by the acoustic parameter data the acoustic parameter data is stored on the memory buffer OWE As noted above a and under the control of the process con-troller 32~ this allows a variable rate data process-99~L

in by circuitries 28 and I This it important in order to allow the entire apparatus to meet the aver-age real time requirements of analyzing the incoming speech, but not to require it to maintain real time processing throughout each speech element on frame by frame basis.

On a frame by frame basis, the maximum them-plate matching and dynamic programming data processing requirements generally occur toward the middle of a speech unit, for example a word. Correspondingly, the processing requirements at the beginning or end of thy speech unit are generally substantially less, in fact, less than the capability of the described apparatus.
Thus, the use of buffer 34 to store acoustic data in lo combination with the capabilities of the circuitries 28 and 30~ allow the average processing rate of the apparatus to be greater than that required for real time operation. Thus real time operation is achieved : : without requiring the hardware to meet instantaneous peak processing rates.

Likelihood Computations he purpose ox the Lockwood computation is to obtain a measure ox the similarity between the in-put acoustic parameters from the principal component transformation and the template patterns which are employed for describing the elements of speech to be recognized. In the illustrated embodiment, each speech element it described by a sequence of template patterns. Associated with each template pattern is a minimum end a maximum duration the combination of the template pattern and the minimum and maximum durations is an acoustic kernel. Typically the speech element Lo I

is a spoken word although the speech element can also be a combination of worms, a single phoneJne, or any o then speech us i t I n accordance with the present invasion thy chosen measure of comparison is a monotonic lung-lion of the probability of a particular speech them-plate given the observation of acoustic parameters at a given frame time. queue acoustic parameters can be thought of a the observation of a random variable.
ho random variables which model the acoustical pane-meters have a probability distribution given by the pure unit of sound intended by the speaker. Since the sound which is no evade is not "pure" and for ease of computation, the probability distribution which is chosen is the Lapels, or double exponential, duster-button. The Lapel distribution is written as, for a single random variable, f lo) - Ce-lx-u~b (En. 33~
where u is She mean of the distribution, b is inversely proportional to the standard deviation, and c is such thwack the density integrates to one In order Jo make the computation easier, the logarithm a the likely-hood rather than thy likelihood itself is chosen as the measure to be employer. (This allows addition rather than multiplication to be employer} in cowlick-feting probabilities. 3 Lucy can be accomplished since the logarithm is a monotonic function of its argue mint. Therefore the measure can be rewritten as:
in l - ha - Ix-ulbo (En. 34) In this computation only u and b need to be known since the natural log of c: is determined by the condition ~L22992~3L

that the density must integrate Jo one. In order to keep most numbers positive, the opus or negative of this measure is employed. The acoustic parameters for a given frame are assumed to be independent so that the f final expression or the measure of likelihood probability becomes Cost R six - us I by Eke. 35) where the sum extends over all acoustic parameters, and R is a f unction of the b ( i) .

In the illustrated embodiment, the likelihood computation is generated for a temple e pattern "on demand for use by the dynamic programming portion of the apparatus. Where, as here, there are two circuit-ryes 2B and 30 operating in parallel, it is possible that the dynamic programming portion of circuitry 29 or I requires a likelihood score from the other Syria quoter 30 or I this would require the transmission of data over bus 24. The dynamic programming "dive-soon ox labor according to the grammar revel and word level griefs described below is chosen to minimize this requirement for thy transmission of data on bus I .

The input to the likelihood calculation, as noted above, consists of the acoustic parameters at a frame time and the statistics (u and b above describe in the template pattern. The template pattern stay tistics convict of a mean (us) and a weight" (by) for each acoustic parameter, and a logarithmic term corresponding to I In the template pattern eta `30 fistic creation, the logarithmic tern usually has been shifted to the right Idivlded by a power of 2) by a quantity which is selected to keep the magnitude of the cost within a sixteen bit integer. For each 1 acoustic parameter the absolute value of the difference between the acoustic parameter and the mean is determined and that quantity is multiplied by the weight associated with the parameters These quantities are added for all of the acoustic patterns; and the sum, if it is less than the maximum sixteen-bit quantity, is shifted to the right by the same quantity applied to the logarithmic term so that the logarithmic term can then be added thereto The result is the "likelihood" or "cost" for the employ pattern at that frame time.
The Dynamic Programming Approach A dynamic programming approach to speech recognition has been used and described elsewhere, for example, in the applicant's Canadian patent numbers 1,182,222, 1,182,223 and 1,182,224 all of which issued February 5, 1985. The dynamic programming approach used herein is an improvement upon the dynamic programming approach described in these Canadian patents.
Referring to Figure 10, the dynamic programming approach can be thought of as finding the best path 150 (that is the path with the minimum score) through a matrix 152 in which the rows are indexed by time indiscrete intervals (corresponding to the discrete time intervals for which measurements are made) and the columns represent elementary units of speech (acoustic kernels in the illustrated embodiment). In theory it is possible to try all possible paths through the matrix and choose the best one. However, there are far too many paths to consider each time and hence in order to find a computational I I

efficient method and apparatus for finding the best path through the matrix, a Markov model for the speech is considered. A stochastic process is said to be Markovian, if ye probability of choosing any given state at a time to depends only upon the state of the system at time t, an not upon the way in which that state, a time t, was reached.

In speech, there is coarticulation, the state by which a given elementary unit of speech affects both those unit which are spoken Before it and after it. units of speech have an effect upon the past because a speaker anticipates what he is going to say.) In order to work around the coarticu-lotion problem within a word, the template patterns are formed for the coarticulated speech units. Lucy method mazes it very difficult to share template be-tweet words which theoretically have the same speech units and is why, in the illustrated embodiment of the invention, the apparatus does not attempt to share such template. For the purposes of the illustrated embodiment, coarticulation between words it ignored.

Thus, the Markovian model for speech is built by including within each state all the information relevant to future decisions. Thus the units of speech are grouped into word because ultimately it is words which will be recognized and it it at thyroid level that syntactl~al constraints cay and, in the thus-treated embodiment, must be applied. syntactical con-straits are represented by a grammar graph 158 (Fig.
5) and it is the grammar graph that makes the model Markovian. Therefore when recognizing utterances, the state space through which the path must be found ~%2~39;21 viewed as logically existing at two levels the gram-mar or syntax level, and the Ford level at which the elementary speech units exist.

At the grammar level the state space consists of a number of connected nodes. A node is a logical point in time which lies either between, before, or after individual words within an utterance. At each node there is a fixed legal vocabulary, each word (or words) of which connects tube node to a new node. A
grammar graph thus consists of a list of arcs, an arc having a starting node, an ending node, and a list of words which cause the transition there between tree Fig. 5). Thor "self" arcs, the starting and ending nodes art the same.) ~15 The second level mentioned above employs word models. A word model is a finite state repro-sensation ox a particular word as spoken by a portico-far speaker. In accordance with the illustrated embodiment, the word model employed is a linear so-quince of acoustic "kernels. A noted above an acoustic kernel is a jingle acoustic template pattern having a minimum and a maximum duration. In the if-lust rated embodiment, a word thus consists of a so-quince of sounds (each represented by a template pat-tern), with a minimum and maximum duration of time being associated with each sound. There is no prove-soon for alternate pronunciations and in accordance with the preferred embodiment of the invention, the method is implemented for speaker dependent speech recognition. Thus, the method relies upon the best estimate that the same speaker says the same word in roughly the same way at all times.

i -~2919Z~

In graph farm, referring the Fig. 6, each word model acoustic kernel has a minimum duration of on" samples and is represented by n identical nodes 160. These are different from the grammar nodes mentioned above The Noah nodes are strung together in a series, each node having a single age coming into and a single arc leaving it. The maximum duration, that is a duration greater than the minimum duration, can be represent Ed by a last node having an arc coming in, an arc leaving, and a Self lop which is the optional dwell time, that us, the difference between the minLnum and maximum durations. All of the arcs have the same acoustic template pattern associated therewith, and for the self loop a count of the number ox times through the loop must be kept in order to accurately maintain all of the information which is needed later during trace back).

the word model graph and the grammar model graph are integrated by replacing, in the grammar graph, each arc with the corresponding word models.
The connection between the grammar nodes and the word ; nod is made by what are called null arcs. pull arcs also allow the apparatus to skip over optional words in the grammar, for example arc 162 twig. 5)0 ~22~32~

Once the needed likelihood computations are availably*, the method proceeds to recognize speech using those likelihood statistics and the allowable syntax graph of the incoming speech. Pictorially then, the graph of the utterance us f first transformed into a lattice, within a matrix, as follows. (see, e.g., Fig. 10) Each stave or node of the graph eon-responds to a column of the lattice and each row of thy lattice corresponds to a specific frame time.
Thus, a lattice state in row I corresponds to time I
and a lattice state in row 3 corresponds to a time J.
Thus, traversing the lattice between row I and row J
corresponds to obtaining the acoustic parameters for the times between and including times Ill and J while traversing, in the gryphon, the archways whose origin node corresponds to the original column and whose end-in node corresponds to the destination column.
: Imposing minimum and maximum durations upon the them-plate patterns corresponds to restricting the vertical :20 distance that the lattice arcs can span between two columns.

The main thrust of the dynamic programming employed in the present invention is, at each row (or time) in the lattice, find the optimal path to a desk Tunisian lattice state using the previously computed costs of the states in the rows between the destine-lion row and the starting row. The "optimal or best path is equivalent to minimizing the cumulative like-Lowe score by choosing the template pattern which maximizes the conditional probability that the speech unit corresponding Jo the template is the correct one given the acoustic parameters at the frame time. That conditional probability is maximized over all of the go "active templates active templates are defined below More specifically, the dynamic prorating performs the following step at each Roy time:

I All nodes are initially set to an initial maximum (16 bit) likelihood score

- 2) Destination nodes of null arcs can inherit their score from the source node ox the arc in zero time.
.10 3) Each "active" kernel in a word on each grammar arc is processed using likelihood computations and duration information and a minimum score for the word at that frame time is determined.
4) If the minimum score for the word is greater than some predetermined thresh-old, the word is deactivated to reduce computations with successive frames.
This is effectively a method for reduce `20 in computation based on the expectation that this path will not be the optimum one.
So The cumulative likelihood score of paths : . at grammar odes, that is, at the end of :25 words leading to a grammar node, is coy-putted.
6) I not all of the kernels of a word are active and toe score of the last active kernel is less than some preselected activation threshold, 'eke next kernel of the word is made alive `
32~

-34- .

Al If the score at the final grammar node of the graph node 200 of Fugue 5, is better (i.e. less) than the score at any . intermediate grammar nuder then an end of utterance has been detected Considered in more detail, at the acoustic kernel level, the dynamic programming uses the "seed score for thy source node of the kernel, the cost of -theta rustic kernel calculated from the current frame, It and the global minimum score of the last frame, to arrive at the likelihood score of a particular kernel at a present frame time. As noted above, the portico-far hardware embodiment described herein determines the likelihood costs "on diamond Thus, as the like-Lydia costs are required by the kernel level dynamic :: programming for a particular frame time, the likely-hood computation is generated. teach node correspond-King to the kernel (recall, referring to Fig. 6, that Jo the kernel it modeled by a plurality of nodes, one for eschew required frame time duration can inherit as the ; "seed score a likelihood score prom a preceding node. (For the first node of a kernel, the seed screen is inherited prom thy last node ox a preceding I: kern unless the first kernel node is the first node along a grammar arc in which case, the "seed core" is inherited from the listened of the best score leading`
into the grammar node.) In addition, thy last node having the kernel can inherit a score from itself (because of the use ox tube self loop in thy word 3Q model in which case the number of times that the self loop has been traversed must be recorded. In order to keep the accumulated costs as small as possible, all of the likelihood scores are normalized by subtracting ~22~

1 the global minimum score (that is, the host score) of the last frame. The new score is then the sum of the inherited score plus the likelihood score or cost for that template at that frame time. When all of the "active"
kernels have been processed, the minimum score for the word is determined and output to the corresponding grammar node.
f the minimum score for the word is greater than a selected deactivation threshold r all kernels a the word are made inactive except for the firs one. This has the effect of reducing the required likelihood and dynamic programming computations at the risk of possibly discarding what might become an optimum path. On the other hand, if the score on the last node of the last active kernel of a word (where not all kernels are active) is less than no activation threshold, then the next kernel of the word is made active. If all of the kernels of the word are active, the destination node of the current grammar arc receives a score which is the minimum score of all the words on all the arcs leading into the grammar - destination node.
The dynamic programming employed here is similar to that described in Canadian patent number 1,182,224 noted above. The primary difference between the dynamic programming employed here and that described in the earlier patent:, is the use here of the null arc and the activation/de~lctivation threshold. The use of the null arc in this Grammar advantageously provides the ability to concatenate words which enables an easier implementation of the apparatus according to thy invention. And, as noted above, the activation/

!
.
I

-36~

deactivation thresholds reduce the computational demands on the apparatus.

In the preferred embodiment of the invention, a partial trace back is employed as a memory saving device. At each frame time, all nodes a a time in the past, equal to the present time Linus the maximum duration of a word, are checked to see whether there it an arc leading from that node to any node at a water time. If there is not, these nodes are elm-noted from the set of nodes which can be used in trace back. Recursively, all nodes in the further past that have arcs leading only to the deleted nodes are in turn deleted. There results therefore a pruned set ; : of tr~ceback nodes which enable less memory to be em-:15 plowed for the trace back process.

Once end-of-utterance has been detected, for : example, when the final grammar node has a better Lowry score than any other grimmer node of the Papa-: fetus, a forced trace back method is employed to de-termite, based upon the result of the dynamic pro-tramming over the length of the utterance, what was actually the best word sequence. The tusk starts at the final node of the grammar graph and progresses backwards, toward the beginning of the utterance, through the best path. Ike output of the traceb~ck is the recognized utterance along with the start and end times, and if desired, a score for each word. Thus, the output of thy feast cost path search is the most probable utterance which is consistent with the spew Swede grammar, the specified word models and Tom plates, and the input acoustic parameters. Further, I
.
-37- .

information necessary to train on the utterance is also available from the system a described below.

TrainingJEnrollment The description presented thus far describes a method for recognizing speech using a plurality of previously formed template pattern. The formation of those template patterns is key to providing a speech recognition system which is both effective and felt-able Consequently, care and attention must be given to the creation of the template patterns. In portico-lark for the illustrated embodiment of the invention, the apparatus is designed to be speaker dependent and hence the template patterns are specifically biased toward the speaker whose speech will be recognized.

Two different methods are described herein-after for adapting the apparatus to a specific speak-or. In a first enrollment method a zero-based enrollment, an initial set of template patterns eon-responding to a new word is generated solely from an input set of acoustic paramours the set of template patterns is created by linearly segmenting thy income in acoustic parameters and deriving the template pat-terns therefrom. ho second method, a training pro-seedier, makes use of a set of acoustic parameters derived from the spookier and the recognition results (that is, speech statistics) of known or assumed utterance which provide an initial trough cut" for the template patterns, to create better templates.
This is accomplished by performing a recognition on each word within a known utterance and using a known worn model.

Lug I

Turning f first to the zero-based enrollment technique, a number of acoustic kernels, each with a minimum and maximum duration, are set for the word based on the duration of the word. The beginning and end of the word are determined, as will be described below and the frames ox acoustic parameters are then proportionally assigned to each kernel (five frames per kernel). The template petrol statistics, the meat and variance, can then be calculated from the acoustic parameters. In the illustrated embodiment, zero-based enrollment employs, for example, ten awakes-tic kernels for a word (an average of So frames in Doreen, each having a minimum duration of two frames and a maximum duration of twelve frames. There results a set of statistics which can be employed for desc~iking the utterance or which can be improved, or example as described below For the training method, the input data include not only the acoustic parameters for the spot ken input word, but training data from a previous least cost path search. This data can include the tentative beginning and ending times for a ward. If no thresholding is performed, and there are no dwell time constraints, the dynamic programming should give the same result as the grammar level dynamic program-Mooney If the dynamic programming at the word level ended where expected, and trace back was performed within the word at the acoustic kernel level. tube trace hack information helps create better template patterns for the word. As a result, a better set of templates can ye achieved. In the illustrated embody-mint, a special grammar consisting of the word alone it built. All of the kernels in the word are made active and word level dynamic programming is performed, -) :
`: .

I

For each word used in the training process, one of the most important aspects of effective training is properly setting the starting time and Lowe ending time for the word. Several procedures have been employed. Once procedure employs a threshold value based-upon the amplitudes of the incoming audio sign Noel Thus for example a system Jan be designed to listen to one pass of the speech and to take the average of the five minimum sample values the average minimum) and the average of the five maximum sample values (the average maximum) during that pass. Then, a threshold is set equal to the sum of four times the average minimum value plus the average maximum value, the entire sum being divided by five. The system then goes through the speech utterance again and after several (for example seven) frame times have amplitudes which exceed the threshold value, the word is declared to haze begun at the first of the frames which exceeded thy threshold value. Similarly, at ~20 the end ox a word, the word is declared to have ended at the end of the last of several lion example seven) frame each of which exceeds the threshold value .
.

A preferred approach however to determine the heginninq and end of an utterance during enrollment is to use a threshold or joker" worn. This: provides for noise immunity a well as an excellent method ox determining the beginnirlg arid end of a sue etch utterance, Referring to Figure it a grammar graph 158 employing toe "joker" word, has a self loop 160 at a node 180, the self loop representing a short silence.
The imaginary sir joker word, represented by an arc 182 between notes 18û an 1~4 has a f iced or constant likelihood cost per frame associated therewith An arc 186 repr~s~ntis~g a long silence, leads from node 184 to a node 188. When silence is the input signal, that is 3 prior to the utterance being made, the self loop (short silence) has a good likelihood C05t per frame and the grammar "staysail at node 180. When speech begins, the cost per frame for silence becomes very poor and the mixed cost of the "joker", along an arc 1~2, is good relative thereto and provides a path from node 180 to node 184. This is the beginning of speech. ThPrafter, the transition from node 184 to 188 denotes the end of speech.

While the grammar graph of Figure 7 works adequately, improved starting and ending times during training can be achieved using two joker" words Referring now to Figure 8, a grammar graph 198 begins at a nude 200, and so long as silence is received the grammar stays in a self loop 202 representing a short silence (which has a low C05t) rather than proceeding along a arc 204 representing the first joker word.
The fist joker word is assigned a relatively high likelihood cost. When speech it encountered, the score for the short silence becomes poor, exceeds the core for the joker work Marc ~04), and causes the grammar to traverse arc 204 to a node 206. At node 206, a second joker word 208 having a slightly lower likelihood cost than the first joker leads away from the node. When long silence is recognized, the grammar traverses arc 210. This indicates thy end of the word. this method gives relatively good noise immunity because of the added "hysteresis" effect of the two joker word sand accurately determines the beginning and end so the incoming isolated utterance employed during the training. Toe two different likelihood costs per frame assigned to the different ~229~

"joker" words have the effect of making sure a word is really detected (by initially favoring silence); and when, once a word is detected, the word it favored (my the second joker) to make sure Salk is really detected to end the word.

The parameters employed in the illustrate embodiment, in connection wit thy grammar of Figure 8 are:
First Second short Long JokerJokerSilenc~ Silence Min. Dwell iamb 1 1 35 Sax. Dwell Tom }01 51 55 likelihood Co~t190Q lB00 -- -typical) Referring to Figure pa, the joker word can : also be employed to provide immunity to sounds such as coughs during neural speech recognition. In this respect/ it is a prowled to normal recognition. In accordance with that aspect of the use of the joker word, a grammar graph 220 has a starting node 222; and so long as silence is received, the grammar stays in a self loop 224 representing short silence which has a low cost) rather than proceeding either along an arc 226 representing a first joker word or an arc 22~
: I leading to the star of the spoke grammar. When .
speech is encountered, the likelihood cost for the first joker word is relatively high, and a transition occurs along the arc 228 into the spoken grammar graph.

. If however non speech such as a cough, occurs, it is the value of the first joker word which provides a likelihood cost that causes the grammar to ( ) ~.~29~
I

traverse arc 226 to a node 230. The grammar stays at node 230, held there by a second joker would on a self arc 232, until a long silence is recognized. The long silence allows the grammar to traverse an arc 234 back to starving node 222. In this manner, the machine returns Jo its quiescent state awaiting a speech input an effectively "ignores" noise inputs as noted above.

System Structure Referring now to Figure 4, according to the preferred embodiment of the invention, the hardware configuration of Figure 1 employs three identical circuit boards; that is, the signal processing circuit board corresponding to circuit 26, a template matching and dynamic programming circuit board corresponding Jo circuit 28 and a second template matching and dynamic programing board corresponding to circuit 30. Each board has thy same configuration the configuration being illustrated as circuitry 218 in Figure 4. The circuitry 218 has three buses an instruction bus 220, a first internal data Gus Z22, and a second internal data bus 224, Kink between data buses 222 and 224 are an arithmetic logic unit (ALUM) 226, a fast 8 bit-by-8 Kit multiplier circuit 228 having associated therewith an accumulator 230 and latching circuits 232 - 25 and 234, a dynamic random access memory TRAM) 236 for example having 128~000 words of sixteen bit memory with latching circuits 238, 240, and 242, and a fax transfer memory 24~ fur example having 2,000 words of sixteen bit memory and associated latches 246, 24B~ and 250. rightable control tore 252 effects control over thy operation of the storage and arithmetic elements.
The control store 252 is a random access Emory having -~22~

an output to bus 222 an providing instruction data on bus 220. The ruble control store, which may be for example a OK by 64 bit RAM, stores the program in-striations and is controlled and addressed by a S micro sequencer 254, for example an AND 2910 micro-sequencer. A pick machine 256 is provided for clock timing, dynamic JAM refresh, and other timing functions as is well known in the art.

This structure employs a double pipeline method which enables the use of relatively inexpensive static RAM for the control store 252~

Important to fast operation for implanting the Lapels transformation to perform likelihood cost generation an inverting register 260 is provided for converting the output of the arithmetic logic unit 226 to a twos complement output when necessary. The out-put of the inverting register is applied to the bus 224.

The operation and programming of the boards 26, 28~ and 30, is con rolled by the particular program code, which programming enables the board when employed as a signal processor 26 to provide the acoustic pram-ethers necessary to perform the template matching and dynamic programming. Similarly, the programming of circuitry 28 and 30 enables the acoustic parameters to be properly processed for generating likelihood cots and or implementing thy dynamic programming.

In operation, as noted above, the template matching and dynamic programming circuits 28 and 30 perform tub ~ikeIihood cost calculations on demand as -I

required by the dynamic programming method This can be accomplished for two reasons. First, the template pattern data necessary to perform all of the likely-hood calculations needed by the dynamic programming portion of a board will be found on that board. (This is a result ox the highly structured grammar graph and the restriction that templates are not shared between words.) Second board, for example board 28, receives the necessary likelihood scores it lacks to complete the dynamic programming for the board. The processor control 32 orchestrates this transfer of information.

The grammar graph referred to above and described in connection with Figure 5, is stored in memories 23S and 244. Because it is stored in a highly structured manner, the data representing one grammar graph can be replaced with the data repro-setting a second grammar graph making the equipment flexible for recognizing different syntax combinations of words or entirely new speech vocabularies yin which case training on the new vocabulary words would have to be done). In the illustrated embodiment, the data replacement it preferably performed by storing multiple grammars in memories 236 and 244 and selecting one ox the grammars under program control. In the illustrated embodiment, the process control 32 can include a disk memory for storing additional grammars.

In addition as noted above, the micro-processor buy for 34 provides tube capability of per-farming variable rate processing. thus, the dynamic programming and likelihood score veneration can fall behind real time somewhat, in the middle ox a speech utterance where the greatest computational requirement ... . . . I_ ~L22~32~

I

occurs, catching up toward the end of the Uranus where fewer calculations need to be made. In this manner, the entire system need not respond as noted above to the peak calculation reknits for real time speech recognition, but need only respond to the average requirements in order to effect real time recognition it the speaker dependent environment illustrated herein.

Other embodiments of the invention, including 10 additions, su~traG~ions~ deletions, and other modîfica-Sheehan of thy preferred described embodiment will be apparent to those skilled in the art and are within the scope of the following claims.
What is claimed is:

Claims (7)

1. In a speech recognition apparatus wherein speech units are each characterized by a sequence of template patterns, and having means for processing a speech input signal for repetitively deriving therefrom, at a frame repetition rate, a plurality of speech recognition acoustic parameters, and means responsive to said acoustic parameters for generating likelihood costs between said acoustic parameters and said speech template patterns, and for processing said likelihood costs for determining the speech units in said speech input signal, a method for inhibiting a response to nonvocabulary utterances in a speech input comprising the steps of repeatedly, at a frame repetition rate, generating acoustic parameters representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, beginning a normal speech recognition process whenever said cost for an active template pattern is better than a predetermined threshold value, and reverting to a non-speech recognition process whenever said cost of said template patterns, including silence, is worse than said predetermined threshold value.

2. The method of claim 1 further comprising the steps of setting a second threshold value and remaining in said non speech recognition process until a likelihood cost of said silence template in better than said second threshold.

3. The method of claim 2 further wherein there are two silence template patterns, a first short silence pattern employed during said reverting step, and a second long silence pattern employed during said remaining step.

4. In a speech recognition apparatus wherein speech units are each characterized by a sequence of template patterns, and having means for processing a speech input signal for repetitively deriving therefrom, at a frame repetition rate, a plurality of speech recognition acoustic parameters, and means responsive to said acoustic parameters for generating likelihood costs between said acoustic parameters and said speech template patterns, and for processing said likelihood costs for determining the speech units in said speech input signal, a method for inhibiting a response to nonvocabulary utterances in a speech input comprising the steps of repeatedly, at a frame repetition rate, generating acoustic parameters representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, employing dynamic programming and a grammer graph for determining in response to said likelihood costs whether there has been a nonvocabulary utterance, said grammar graph having a normal speech recognition branch and a non-speech recognition branch, and said employing step determining and selecting, using said dynamic programming, the better scoring of said speech recognition and said non-speech recognition branches.

5. The method of claim 4 further comprising the steps of assigning a fixed predetermined threshold score to an entrance arc of said non-speech recognition branch of said grammar, assigning a second fixed predetermined threshold

Claim 5 continued... -48-score to a self loop arc of said non-recognition branch for providing a path to remain in said non-recognition branch, and providing a silence arc leading from said self loop arc to the beginning of said entrance arc.

6. The method of claim 5 further comprising the step of assigning a self loop silence arc at a grammar node from which said entrance arc originates.

7. The method of claim 6 further comprising the steps of providing said silence arc with a good score for a long silence, and providing said self loop silence arc with a good score for a short silence.