EP1078354B1 - Verfahren und anordnung zur bestimmung spektraler sprachcharakteristika in einer gesprochenen äusserung - Google Patents
Verfahren und anordnung zur bestimmung spektraler sprachcharakteristika in einer gesprochenen äusserung Download PDFInfo
- Publication number
- EP1078354B1 EP1078354B1 EP99929088A EP99929088A EP1078354B1 EP 1078354 B1 EP1078354 B1 EP 1078354B1 EP 99929088 A EP99929088 A EP 99929088A EP 99929088 A EP99929088 A EP 99929088A EP 1078354 B1 EP1078354 B1 EP 1078354B1
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- EP
- European Patent Office
- Prior art keywords
- transformation
- utterance
- wavelet
- speech
- speaker
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- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- 230000003595 spectral effect Effects 0.000 title claims abstract description 20
- 238000000034 method Methods 0.000 title claims description 16
- 230000014509 gene expression Effects 0.000 title abstract 4
- 230000009466 transformation Effects 0.000 claims abstract description 63
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 13
- 238000003786 synthesis reaction Methods 0.000 claims abstract description 13
- 238000001914 filtration Methods 0.000 claims 1
- 230000006870 function Effects 0.000 description 8
- 230000007704 transition Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 230000001755 vocal effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000004090 dissolution Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
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- 230000002123 temporal effect Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L13/00—Speech synthesis; Text to speech systems
- G10L13/06—Elementary speech units used in speech synthesisers; Concatenation rules
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
Definitions
- the invention relates to a method and an arrangement for Determination of spectral speech characteristics in one spoken utterance.
- a wavelet transformation is known from [1].
- Wavelet transformation is through a wavelet filter ensures that a high pass share and a Low-pass portion of a subsequent transformation stage Signal of a current transformation stage complete restore. It is done by a Transformation stage to the next a reduction of Dissolution of the high-pass component or low-pass component Technical term: "Subsampling"). In particular, by that Subsampling the number of transformation levels finally.
- US-A-5528725 discloses a method for speech recognition using wavelet transformations.
- EP-A-0519802 discloses a method of speech synthesis which speaker-specific characteristics with regard to a a natural sounding sequence of speech sounds adapts.
- the object of the invention is a method and an arrangement for determining spectral Specify language characteristics, with the help in particular a natural-looking synthetic speech output can be determined is.
- a method for Determination of spectral speech characteristics in one spoken utterance. This becomes the spoken utterance digitized and subjected to a wavelet transformation. Using different transformation levels of the wavelet transformation become the speaker-specific Characteristics determined.
- the individual high-pass components or low-pass components stand for predefined ones speaker-specific characteristics, both High pass component as well as low pass component of a respective one Transformation level, i.e. the respective characteristic, can be modified separately from other characteristics. If you use the inverse wavelet transformation from the respective high-pass and low-pass proportions of the individual Transformation levels back to the original signal together, this ensures that exactly what you want Characteristic has been changed. It is therefore possible to change certain predetermined characteristics of the utterance, without affecting the rest of the utterance.
- One embodiment is that before the wavelet transformation the utterance windowed, that is, a given one Set of samples cut out, and in the Frequency range is transformed. This will in particular a Fast Fourier Transform (FFT) is applied.
- FFT Fast Fourier Transform
- Another embodiment is that a High-pass component of a transformation stage in a real part and split an imaginary part.
- the high pass portion of the Wavelet transformation corresponds to the difference signal between the current low-pass component and the low-pass component the previous transformation level.
- further training consists in the number of transformation stages of the wavelet transformation to be carried out by determining that in the last Transformation level, which consists of cascaded Low passports exist, contain a steady portion of the utterance is. Then the signal as a whole can be represented by its Wavelet coefficients. This corresponds to the complete one Transformation of the information of the signal section in the Wavelet space.
- the speaker-specific characteristics a) to c) are in speech synthesis of great importance.
- An advantage of the invention is that the spectral Envelope reflects the speaker's articulation tract and not, e.g. a pole position model, on formants is supported. Go further with the wavelet transformation as a nonparametric representation, no data is lost that The utterance can always be completely reconstructed. From the individual transformation levels of the wavelet transformation resulting data are linear from each other independently, can thus be influenced separately and later on to the influenced utterance - lossless - be put together.
- an arrangement for determining spectral Speech characteristics indicated a processor unit has, which is set up such that an utterance can be digitized. Then the utterance becomes a Wavelet transform and subjected to different levels of transformation speaker-specific characteristics determined.
- the standard deviation ⁇ is determined by the Predeterminable position of the sideband minimum 101 in FIG. 1.
- Equation (1) The constant c from equation (1) is used to normalize the complex wavelet function: in which ⁇ called the conjugate complex wavelet function.
- a signal 301 is filtered both by a high pass HP1 302 and by a low pass TP1 305.
- subsampling takes place, ie the number of values to be stored is reduced per filter.
- An inverse wavelet transformation ensures that the original signal 301 can be reconstructed from the low-pass component TP1 305 and the high-pass component HP1 304.
- HP1 302 is separated according to real part Re1 303 and Imaginary part Im1 304 filtered.
- the signal 310 after the low pass filter TP1 305 is again both by a high pass HP2 306 and by a Filtered low pass TP2 309.
- the HP2 306 high pass includes again a real part Re2 307 and an imaginary part Im2 308.
- Das Signal after the second transformation stage 311 is again filtered, etc.
- FIG. 4 shows various transformation stages of the wavelet transformation, divided into low-pass components (FIGS. 4A, 4C and 4E) and high-pass components (FIGS. 4B, 4D and 4F).
- the fundamental frequency is spoken utterance evident.
- the fluctuations in the amplitude is clearly a predominant periodicity in Wavelet-filtered spectrum to recognize the fundamental frequency of the speaker.
- the fundamental frequency it is possible given utterances in speech synthesis each other adapt or use appropriate statements from a database to determine given utterances.
- Fig.4C In the low-pass portion of Fig.4C are as pronounced minima and Maxima the formants of the speech signal section (the length of the speech signal section corresponds approximately to twice Fundamental frequency).
- the formants represent Resonance frequencies in the speaker's vocal tract. The clear one Representability of the formants enables an adjustment and / or selection of suitable sound modules at concatenative speech synthesis.
- the three speaker-specific characteristics mentioned are thus identified and targeted for speech synthesis to be influenced. It is particularly important that manipulation in the inverse wavelet transform of a single speaker-specific characteristic only this influences the other perceptually relevant variables stay untouched. Thus, the fundamental frequency can be targeted can be adjusted without changing the smokiness of the voice being affected.
- Another possible application is the selection of one suitable sound section for concatenative linking with another sound section, both sound sections originally from different speakers in different Contexts were included.
- determination spectral Speech characteristics can be a more suitable one to be linked Phonetic section can be found as with the characteristics Criteria are known that allow a comparison of Sound sections among themselves and thus a selection of the matching sound section automatically according to certain specifications enable.
- a database is created with a predetermined amount of naturally-spoken language from different speakers, sound sections in the naturally-spoken language being identified and stored. There are numerous representatives for the different sound sections of a language that the database can access.
- the sound sections are in particular phonemes of a language or a series of such phonemes. The smaller the section of the sound, the greater the possibilities when composing new words. For example, the German language contains a predetermined amount of approximately 40 phonemes, which are sufficient for the synthesis of almost all words in the language. Different acoustic contexts must be taken into account, depending on the word in which the respective phoneme appears.
- FIG. 5 shows two sounds A 507 and B 508 by way of example shown, each of the individual sound sections 505 and 506 exhibit. Lute A 507 and B 508 are from a spoken utterance, whereby the sound A 507 clearly is different from the sound B 508. A dividing line 509 indicates where the A 507 sound should be linked to the B 508 sound. In the present case, the first three sound sections should of the sound A 507 with the last three sound sections of the According to B 508 can be linked concatenatively.
- a variant consists in an abrupt transition along the the dividing line 509 divided sounds. However, this happens the discontinuities mentioned, which the human ear as distracting. If you put together a sound C, that the sound sections within a transition area 501 or 502 are taken into account, where a spectral Distance between two assignable Sound sections in the respective transition area 501 or 502 is adjusted (gradual transition between the According sections). As the distance measure is used especially in the wavelet space the Euclidean distance between the relevant coefficients in this area.
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- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Multimedia (AREA)
- Electrically Operated Instructional Devices (AREA)
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Description
Die Schwingung des Hochpaßanteils der ersten oder der zweiten Transformationsstufe der Wavelet-Transformation läßt die Grundfrequenz der Äußerung erkennen. Die Grundfrequenz zeigt an, ob der Sprecher ein Mann oder einen Frau ist.
Die spektrale Hüllkurve enthält Information über eine Transferfunktion des Vokaltrakts bei der Artikulation. In einem stimmhaften Bereich wird die spektrale Hüllkurve von den Formanten dominiert. Der Hochpaßanteil einer höheren Transformationsstufe der Wavelet-Transformation enthält diese spektrale Hüllkurve.
Die Rauchigkeit in einer Stimme wird als negative Steigung im Verlauf des vorletzten Tiefpaßanteils sichtbar.
- Fig.1
- eine Wavelet-Funktion;
- Fig.2
- eine Wavelet-Funktion, unterteilt nach Realteil und Imaginärteil;
- Fig.3
- eine kaskadierte Filterstruktur, die die Transformationsschritte der Wavelet-Transformation darstellt;
- Fig.4
- Tiefpaßanteile und Hochpaßanteile unterschiedlicher Transformationsstufen;
- Fig.5
- Schritte der konkatenativen Sprachsynthese.
- f
- die Frequenz,
- σ
- eine Standardabweichung und
- c
- eine vorgegebene Normierungskonstante
Claims (10)
- Verfahren zur Bestimmung spektraler Sprachcharakteristika in einer gesprochenen Äußerung,a) bei dem die Äußerung digitalisiert wird,b) bei dem die digitalisierte Äußerung einer Wavelet-Transformation unterzogen wird,c) bei dem anhand unterschiedlicher Transformationsstufen der Wavelet-Transformation die sprecherspezifischen Charakteristika bestimmt werden.
- Verfahren nach Anspruch 1,
bei dem vor der Wavelet-Transformation eine gefensterte Transformation der digitalisierten Äußerung in einen Frequenzbereich durchgeführt wird. - Verfahren nach Anspruch 2,
bei dem die Transformation in den Frequenzbereich mittels Fast-Fourier-Transformation durchgeführt wird. - Verfahren nach einem der vorhergehenden Ansprüche,
bei dem in jeder Stufe der Wavelet-Transformation ein Tiefpaßanteil und ein Hochpaßanteil eines zu transformierenden Signals ermittelt werden. - Verfahren nach einem der vorhergehenden Ansprüche,
bei dem ein Hochpaßanteil nach einem Realteil und einem Imaginärteil unterteilt wird. - Verfahren nach einem der vorhergehenden Ansprüche,
bei dem die Wavelet-Transformation mehrere Transformationsstufen umfaßt, wobei die letzte Transformationsstufe einen Gleichanteil der Äußerung in einer der Anzahl Transformationsstufen entsprechenden wiederholten Tiefpaßfilterung liefert. - Verfahren nach einem der vorhergehenden Ansprüche,
bei dem die sprecherspezifischen Charakteristika bestimmt sind durch:a) eine Grundfrequenz der gesprochenen Äußerung;b) spektrale Hüllkurve;c) einer Rauchigkeit der gesprochenen Äußerung. - Verwendung des Verfahrens nach einem der Ansprüche 1 bis 7 zur Sprachsynthese,
wobei einzelne sprecherspezifische Charakteristika im Hinblick auf eine natürlich klingende Aneinanderreihung von Sprachlauten angepaßt werden. - Verwendung des Verfahrens nach einem der Ansprüche 1 bis 7 zur Sprachsynthese,
wobei aus einer vorgegebenen Datenmenge diejenigen Sprachlaute anhand einzelner spektraler Sprachcharakteristika ausgewählt werden, die eine natürlich klingende Aneinanderreihung von Sprachlauten gewährleisten. - Anordnung zur Bestimmung spektraler Sprachcharakteristika in einer gesprochenen Äußerung
mit einer Prozessoreinheit, die derart eingerichtet ist, daß folgende Schritte durchführbar sind:a) die Äußerung wird digitalisiert;b) die digitalisierte Äußerung wird einer Wavelet-Transformation unterzogen;c) anhand unterschiedlicher Transformationsstufen der Wavelet-Transformation werden die sprecherspezifischen Charakteristika bestimmt.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19821031 | 1998-05-11 | ||
DE19821031 | 1998-05-11 | ||
PCT/DE1999/001308 WO1999059134A1 (de) | 1998-05-11 | 1999-05-03 | Verfahren und anordnung zur bestimmung spektraler sprachcharakteristika in einer gesprochenen äusserung |
Publications (2)
Publication Number | Publication Date |
---|---|
EP1078354A1 EP1078354A1 (de) | 2001-02-28 |
EP1078354B1 true EP1078354B1 (de) | 2002-03-20 |
Family
ID=7867382
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP99929088A Expired - Lifetime EP1078354B1 (de) | 1998-05-11 | 1999-05-03 | Verfahren und anordnung zur bestimmung spektraler sprachcharakteristika in einer gesprochenen äusserung |
Country Status (6)
Country | Link |
---|---|
EP (1) | EP1078354B1 (de) |
JP (1) | JP2002515608A (de) |
AT (1) | ATE214831T1 (de) |
DE (1) | DE59901018D1 (de) |
ES (1) | ES2175988T3 (de) |
WO (1) | WO1999059134A1 (de) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE10031832C2 (de) | 2000-06-30 | 2003-04-30 | Cochlear Ltd | Hörgerät zur Rehabilitation einer Hörstörung |
US8554550B2 (en) * | 2008-01-28 | 2013-10-08 | Qualcomm Incorporated | Systems, methods, and apparatus for context processing using multi resolution analysis |
JP6251145B2 (ja) * | 2014-09-18 | 2017-12-20 | 株式会社東芝 | 音声処理装置、音声処理方法およびプログラム |
JP2018025827A (ja) * | 2017-11-15 | 2018-02-15 | 株式会社東芝 | 対話システム |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR2678103B1 (fr) * | 1991-06-18 | 1996-10-25 | Sextant Avionique | Procede de synthese vocale. |
GB2272554A (en) * | 1992-11-13 | 1994-05-18 | Creative Tech Ltd | Recognizing speech by using wavelet transform and transient response therefrom |
JP3093113B2 (ja) * | 1994-09-21 | 2000-10-03 | 日本アイ・ビー・エム株式会社 | 音声合成方法及びシステム |
-
1999
- 1999-05-03 EP EP99929088A patent/EP1078354B1/de not_active Expired - Lifetime
- 1999-05-03 DE DE59901018T patent/DE59901018D1/de not_active Expired - Fee Related
- 1999-05-03 ES ES99929088T patent/ES2175988T3/es not_active Expired - Lifetime
- 1999-05-03 WO PCT/DE1999/001308 patent/WO1999059134A1/de active IP Right Grant
- 1999-05-03 JP JP2000548866A patent/JP2002515608A/ja active Pending
- 1999-05-03 AT AT99929088T patent/ATE214831T1/de not_active IP Right Cessation
Also Published As
Publication number | Publication date |
---|---|
DE59901018D1 (de) | 2002-04-25 |
WO1999059134A1 (de) | 1999-11-18 |
JP2002515608A (ja) | 2002-05-28 |
ATE214831T1 (de) | 2002-04-15 |
EP1078354A1 (de) | 2001-02-28 |
ES2175988T3 (es) | 2002-11-16 |
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