WO2001031434A2 - Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe - Google Patents
Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe Download PDFInfo
- Publication number
- WO2001031434A2 WO2001031434A2 PCT/DE2000/003753 DE0003753W WO0131434A2 WO 2001031434 A2 WO2001031434 A2 WO 2001031434A2 DE 0003753 W DE0003753 W DE 0003753W WO 0131434 A2 WO0131434 A2 WO 0131434A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- fundamental frequency
- macro
- sequences
- frequency sequences
- default
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 230000006870 function Effects 0.000 claims description 24
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 238000004088 simulation Methods 0.000 claims description 10
- 230000002194 synthesizing effect Effects 0.000 claims description 4
- 230000001537 neural effect Effects 0.000 abstract 2
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000003786 synthesis reaction Methods 0.000 description 3
- 230000007704 transition Effects 0.000 description 3
- ANVAOWXLWRTKGA-NTXLUARGSA-N (6'R)-beta,epsilon-carotene Chemical compound CC=1CCCC(C)(C)C=1\C=C\C(\C)=C\C=C\C(\C)=C\C=C\C=C(/C)\C=C\C=C(/C)\C=C\[C@H]1C(C)=CCCC1(C)C ANVAOWXLWRTKGA-NTXLUARGSA-N 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
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
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/90—Pitch determination of speech signals
-
- 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
- G10L25/78—Detection of presence or absence of voice signals
- G10L2025/783—Detection of presence or absence of voice signals based on threshold decision
-
- 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
- G10L25/30—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique using neural networks
Definitions
- the invention relates to a method for determining the time profile of a fundamental frequency of a speech output to be synthesized.
- the fundamental frequency is composed of individual fundamental frequency patterns
- a metallic, mechanical sound is still generated which can be clearly distinguished from a natural voice.
- the basic frequency is set with a neural network, the voice sounds more natural, but is somewhat dull.
- the invention is therefore based on the object of providing a method for determining the time profile of a basic frequency of a speech output to be synthesized, which gives the speech output a natural sound which is very similar to a human voice.
- the method according to the invention for determining the time profile of a basic frequency of a speech output to be synthesized comprises the following steps:
- the present invention is based on the knowledge that the determination of the course of a fundamental frequency by means of a neural network produces the macrostructure of the temporal course of a fundamental frequency very similar to the course of the fundamental frequency of a natural language, and the fundamental frequency sequences stored in a database are very similar to the microstructure of the Play back the fundamental frequency of a natural language.
- the combination according to the invention thus achieves an optimal determination of the course of the fundamental frequency, which is much more similar to that of natural language, both in the macrostructure and in the microstructure, than with a fundamental frequency generated with the previously known methods. In this way, a considerable approximation of the synthetic speech output to a natural language is achieved.
- the synthetic language generated in this way is very similar to natural language and can hardly be distinguished from it.
- the deviation between the simulation macro segment and the default macro segment is preferably determined by means of a cost function which is weighted in such a way that with small deviations from the basic frequency of the default macro segment only a small deviation is determined, the predetermined deviations being strong until they are reached when predetermined limit frequency differences are exceeded of a saturation value increase.
- This non-linearity represents the non-linear behavior of human hearing.
- deviations are weighted the weaker the closer they are to the edge of a syllable.
- the default macro segment is preferably reproduced by generating a plurality of fundamental frequency sequences for each microprosodic unit, combinations of fundamental frequency sequences being evaluated both with regard to the deviation from the default macro segment and with regard to pairwise tuning. Depending on the result of these two evaluations (deviation from the default macro segment, coordination between neighboring fundamental frequency sequences), a corresponding selection of a combination of fundamental frequency sequences is then made.
- these paired adjustments of the fundamental frequency sequences within a syllable are weighted more strongly than at the edge area of the syllable.
- the syllable nucleus is decisive for aural impression.
- 3 shows the course of a basic frequency consisting of several macro segments, 4 schematically simplified the structure of a neural network,
- FIG. 6 shows a method for synthesizing speech which is based on the method according to the invention.
- This method is implemented in the form of a computer program that is started with a step S1.
- step S2 a text is entered that is in the form of an electronically readable text file.
- step S3 a sequence of phonemes, that is to say a phonetic sequence, is created, the individual graphemes of the text, that is to say one or more letters each, to which a phoneme is assigned, being determined.
- the phonemes assigned to the individual graphemes are then determined, as a result of which the phoneme sequence is determined.
- step S4 an emphasis structure is determined, ie it is determined how strongly the individual phonemes are to be emphasized.
- the emphasis structure is shown in FIG. 1 a by means of a time line using the word "stop". Accordingly, the graphem "st" is the emphasis level 1, the grapheme “o” the emphasis level 0.3 and the grapheme “p” the emphasis level 0.5 been assigned. The duration of the individual phonemes is then determined (S5).
- step S6 the time course of the fundamental frequency is determined, which is explained in more detail below.
- a wave file can be generated based on the phonemes and the fundamental frequency (JS7).
- the wave file is converted into acoustic signals by means of an acoustic output unit and a loudspeaker (S8), which ends the speech output (S9).
- the time course of the fundamental frequency of the speech output to be synthesized is generated by means of a neural network in combination with fundamental frequency sequences stored in a database.
- step S6 of FIG. 6 is shown in greater detail in FIG. 5 in a flow chart.
- This method for determining the time profile of the fundamental frequency is a subroutine of the program shown in FIG. 6.
- the subroutine is started with step SlO.
- Step S11 determines a default macro segment of the fundamental frequency using a neural network.
- a neural network is shown schematically simplified in FIG. 4.
- the neural network has at an input layer I nodes for inputting a phonetic linguistic unit PE of the text to be synthesized and a context Kl, Kr to the left and right of the phonetic linguistic unit.
- the phonetic linguistic unit consists, for example, of a phrase, a word or a syllable of the text to be synthesized, for which the default macro element of the fundamental frequency is to be determined.
- the left context K1 and the right context Kr each represent a text section to the left and right of the phonetic linguistic unit PE.
- the data entered with the phonetic unit include the corresponding phoneme sequence, emphasis structure and the duration of the individual phonemes.
- the information entered with the left or right context comprises at least the phoneme sequence, although it may be expedient to also enter the emphasis structure and / or the duration of the sound.
- the length of the left and right context can correspond to the length of the phonetic linguistic unit PE, that is to say again a phrase, a word or a syllable. However, it can also be expedient to provide a longer context of, for example, two or three words as a left or right context.
- These inputs Kl, PE and Kr are processed in a hidden layer VS and output on an output layer 0 as the default macro segment VG of the fundamental frequency.
- Such a default macro segment for the word “stop” is shown in FIG. 1b.
- This default macro segment has a typical triangular shape, which begins with an increase and ends with a somewhat shorter decrease.
- the microsegments corresponding to the default macro segment are determined in steps S12 and S13.
- step S12 data are read from a data base m in which fundamental frequency sequences assigned to graphemes are stored, m generally having a multiplicity of fundamental frequency sequences for each grapheme.
- m generally having a multiplicity of fundamental frequency sequences for each grapheme.
- FIG. 1c such fundamental frequency sequences for the graphemes “st”, “o "and” p "are shown schematically, only a small number of fundamental frequency sequences being shown to simplify the drawing.
- These fundamental frequency sequences can in principle be combined with one another as desired.
- the possible combinations of these fundamental frequency sequences are evaluated using a cost function. This process step is carried out using the Viterbi algorithm.
- a cost factor Kf is calculated using the following cost function:
- the cost function has two terms, a local cost function lok (k 1 ) and a link cost function Ver (k 1D , k n , j +1).
- the link cost function is used to evaluate the coordination between the i-th fundamental frequency of the j-th phoneme and the n-th fundamental frequency sequence of the j + l-th phoneme.
- the local cost function has the following form, for example:
- the local cost function is thus an integral over the time range from the beginning ta of a phoneme to the end te of the phoneme over the square of the difference between the basic frequency f v specified by the default macro segment and the i th basic frequency sequence of the j th phoneme.
- This local cost function thus determines a positive value of the deviation between the respective fundamental frequency sequence and the fundamental frequency of the default macro segment.
- this cost function is very easy to implement and, due to the parabolic property, generates an assessment that is similar to that of human hearing, since minor deviations around the default sequence f v are assessed as small, whereas larger deviations are assessed progressively.
- the local cost function is provided with a weighting term that leads to the function curve shown in FIG. 2.
- the diagram from FIG. 2 shows the value of the local cost function lok (f 1D ) as a function of the logarithm of the frequency f ⁇ of the lth fundamental frequency sequence of the ⁇ th phoneme.
- the diagram shows that deviations from the default frequency f v within certain cut-off frequencies GF1, GF2 are assessed only slightly, with a further deviation causing a sharply increasing increase up to a threshold value SW.
- Such a weighting corresponds to human hearing, which hardly perceives slight frequency deviations but registers this as a clear difference from certain frequency differences.
- the linkage cost function is used to evaluate how well two consecutive fundamental frequency sequences are coordinated.
- the frequency difference at the junction of the two fundamental frequency sequences is evaluated, with "the greater the difference at the end of the previous fundamental frequency sequence from the frequency at the beginning of the subsequent fundamental frequency sequences, the greater the output value of the linkage cost function further parameters are taken into account which, for example, reflect the continuity of the transition or the like.
- step S14 the program flow jumps back to step S11, otherwise the program flow branches to step S15, with which the individual simulation macro segments of the fundamental frequency are assembled.
- step S16 the connection points of the individual simulation macro segments are matched to one another, as shown in FIG. 3.
- the frequencies left fi and right f r are matched to one another by the connecting points V, the end regions of the simulation macro segments preferably being changed in such a way that the frequencies f ⁇ and f r have the same value.
- the transition can preferably also be smoothed and / or made continuous.
- a course of a fundamental frequency can thus be generated which is very similar to the fundamental frequency of a natural language, since larger context areas can be easily detected and evaluated by means of the neural network (macro structure) and at the same time by means of the fundamental frequency sequences stored in the database finest structures of the fundamental frequency curve can be generated according to natural language (microstructure). This enables speech output with a much more natural sound than in previously known methods.
Landscapes
- Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Machine Translation (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Claims
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP00984858A EP1224531B1 (de) | 1999-10-28 | 2000-10-24 | Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe |
US10/111,695 US7219061B1 (en) | 1999-10-28 | 2000-10-24 | Method for detecting the time sequences of a fundamental frequency of an audio response unit to be synthesized |
JP2001533505A JP4005360B2 (ja) | 1999-10-28 | 2000-10-24 | 合成すべき音声応答の基本周波数の時間特性を定めるための方法 |
DE50008976T DE50008976D1 (de) | 1999-10-28 | 2000-10-24 | Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19952051.8 | 1999-10-28 | ||
DE19952051 | 1999-10-28 |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2001031434A2 true WO2001031434A2 (de) | 2001-05-03 |
WO2001031434A3 WO2001031434A3 (de) | 2002-02-14 |
Family
ID=7927243
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/DE2000/003753 WO2001031434A2 (de) | 1999-10-28 | 2000-10-24 | Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe |
Country Status (5)
Country | Link |
---|---|
US (1) | US7219061B1 (de) |
EP (1) | EP1224531B1 (de) |
JP (1) | JP4005360B2 (de) |
DE (1) | DE50008976D1 (de) |
WO (1) | WO2001031434A2 (de) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE10230884A1 (de) * | 2002-07-09 | 2004-02-05 | Siemens Ag | Vereinigung von Prosodiegenerierung und Bausteinauswahl bei der Sprachsynthese |
US7167824B2 (en) | 2002-02-14 | 2007-01-23 | Sail Labs Technology Ag | Method for generating natural language in computer-based dialog systems |
Families Citing this family (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP4264030B2 (ja) * | 2003-06-04 | 2009-05-13 | 株式会社ケンウッド | 音声データ選択装置、音声データ選択方法及びプログラム |
JP2005018036A (ja) * | 2003-06-05 | 2005-01-20 | Kenwood Corp | 音声合成装置、音声合成方法及びプログラム |
WO2005119650A1 (ja) * | 2004-06-04 | 2005-12-15 | Matsushita Electric Industrial Co., Ltd. | 音声合成装置 |
US10453479B2 (en) * | 2011-09-23 | 2019-10-22 | Lessac Technologies, Inc. | Methods for aligning expressive speech utterances with text and systems therefor |
US10109014B1 (en) | 2013-03-15 | 2018-10-23 | Allstate Insurance Company | Pre-calculated insurance premiums with wildcarding |
CN105357613B (zh) * | 2015-11-03 | 2018-06-29 | 广东欧珀移动通信有限公司 | 音频输出设备播放参数的调整方法及装置 |
CN106653056B (zh) * | 2016-11-16 | 2020-04-24 | 中国科学院自动化研究所 | 基于lstm循环神经网络的基频提取模型及训练方法 |
CN108630190B (zh) * | 2018-05-18 | 2019-12-10 | 百度在线网络技术(北京)有限公司 | 用于生成语音合成模型的方法和装置 |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2325599A (en) * | 1997-05-22 | 1998-11-25 | Motorola Inc | Speech synthesis with prosody enhancement |
US5913194A (en) * | 1997-07-14 | 1999-06-15 | Motorola, Inc. | Method, device and system for using statistical information to reduce computation and memory requirements of a neural network based speech synthesis system |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1057625C (zh) | 1994-04-28 | 2000-10-18 | 摩托罗拉公司 | 使用神经网络变换文本为声频信号的方法 |
US5787387A (en) * | 1994-07-11 | 1998-07-28 | Voxware, Inc. | Harmonic adaptive speech coding method and system |
JPH10153998A (ja) * | 1996-09-24 | 1998-06-09 | Nippon Telegr & Teleph Corp <Ntt> | 補助情報利用型音声合成方法、この方法を実施する手順を記録した記録媒体、およびこの方法を実施する装置 |
US6064960A (en) * | 1997-12-18 | 2000-05-16 | Apple Computer, Inc. | Method and apparatus for improved duration modeling of phonemes |
US6078885A (en) * | 1998-05-08 | 2000-06-20 | At&T Corp | Verbal, fully automatic dictionary updates by end-users of speech synthesis and recognition systems |
AU772874B2 (en) * | 1998-11-13 | 2004-05-13 | Scansoft, Inc. | Speech synthesis using concatenation of speech waveforms |
US7222075B2 (en) * | 1999-08-31 | 2007-05-22 | Accenture Llp | Detecting emotions using voice signal analysis |
-
2000
- 2000-10-24 WO PCT/DE2000/003753 patent/WO2001031434A2/de active IP Right Grant
- 2000-10-24 US US10/111,695 patent/US7219061B1/en not_active Expired - Fee Related
- 2000-10-24 JP JP2001533505A patent/JP4005360B2/ja not_active Expired - Fee Related
- 2000-10-24 DE DE50008976T patent/DE50008976D1/de not_active Expired - Lifetime
- 2000-10-24 EP EP00984858A patent/EP1224531B1/de not_active Expired - Lifetime
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
GB2325599A (en) * | 1997-05-22 | 1998-11-25 | Motorola Inc | Speech synthesis with prosody enhancement |
US5913194A (en) * | 1997-07-14 | 1999-06-15 | Motorola, Inc. | Method, device and system for using statistical information to reduce computation and memory requirements of a neural network based speech synthesis system |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7167824B2 (en) | 2002-02-14 | 2007-01-23 | Sail Labs Technology Ag | Method for generating natural language in computer-based dialog systems |
DE10230884A1 (de) * | 2002-07-09 | 2004-02-05 | Siemens Ag | Vereinigung von Prosodiegenerierung und Bausteinauswahl bei der Sprachsynthese |
DE10230884B4 (de) * | 2002-07-09 | 2006-01-12 | Siemens Ag | Vereinigung von Prosodiegenerierung und Bausteinauswahl bei der Sprachsynthese |
Also Published As
Publication number | Publication date |
---|---|
EP1224531B1 (de) | 2004-12-15 |
WO2001031434A3 (de) | 2002-02-14 |
US7219061B1 (en) | 2007-05-15 |
JP2003513311A (ja) | 2003-04-08 |
EP1224531A2 (de) | 2002-07-24 |
DE50008976D1 (de) | 2005-01-20 |
JP4005360B2 (ja) | 2007-11-07 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
AT400646B (de) | Sprachsegmentkodierungs- und tonlagensteuerungsverfahren für sprachsynthesesysteme und synthesevorrichtung | |
DE60214358T2 (de) | Zeitskalenmodifikation von signalen mit spezifischem verfahren je nach ermitteltem signaltyp | |
DE69925932T2 (de) | Sprachsynthese durch verkettung von sprachwellenformen | |
DE69010941T2 (de) | Verfahren und Einrichtung zur automatischen Bestimmung von phonologischen Regeln für ein System zur Erkennung kontinuierlicher Sprache. | |
DE69506037T2 (de) | Audioausgabeeinheit und Methode | |
DE3783154T2 (de) | Spracherkennungssystem. | |
DE2115258C3 (de) | Verfahren und Anordnung zur Sprachsynthese aus Darstellungen von individuell gesprochenen Wörtern | |
DE602005002706T2 (de) | Verfahren und System für die Umsetzung von Text-zu-Sprache | |
DE69627865T2 (de) | Sprachsynthesizer mit einer datenbank für akustische elemente | |
DE19942178C1 (de) | Verfahren zum Aufbereiten einer Datenbank für die automatische Sprachverarbeitung | |
DE2920298A1 (de) | Binaere interpolatorschaltung fuer ein elektronisches musikinstrument | |
DE2736082A1 (de) | Elektronisches geraet zur phonetischen synthetisierung menschlicher sprache (sprach-synthesizer) | |
WO2001031434A2 (de) | Verfahren zum bestimmen des zeitlichen verlaufs einer grundfrequenz einer zu synthetisierenden sprachausgabe | |
DE69609531T2 (de) | Sprachanpassungsgerät | |
DE4491015C2 (de) | Verfahren zum Erzeugen eines Spektralrauschbewertungsfilters zur Verwendung in einem Sprachcoder | |
DE19861167A1 (de) | Verfahren und Vorrichtung zur koartikulationsgerechten Konkatenation von Audiosegmenten sowie Vorrichtungen zur Bereitstellung koartikulationsgerecht konkatenierter Audiodaten | |
DE69425874T2 (de) | Verfahren und Anordnung zur automatischen Extraktion prosodischer Information | |
DE69318209T2 (de) | Verfahren und Anordnung zur Sprachsynthese | |
DE69717933T2 (de) | Verfahren zur Änderung der Grundfrequenz einer V(okal)-K(onsonant)-V(okal) Phonemketten-Wellenform und Vorrichtung zur Klangsynthese aus einer Folge von VKV Phonemketten-Wellenformen | |
WO2001086634A1 (de) | Verfahren zum erzeugen einer sprachdatenbank für einen zielwortschatz zum trainieren eines spracherkennungssystems | |
WO2000054254A1 (de) | Verfahren und anordnung zur bestimmung eines repräsentativen lautes | |
EP0058130B1 (de) | Verfahren zur Synthese von Sprache mit unbegrenztem Wortschatz und Schaltungsanordnung zur Durchführung des Verfahrens | |
DE60305944T2 (de) | Verfahren zur synthese eines stationären klangsignals | |
DE60311482T2 (de) | Verfahren zur steuerung der dauer bei der sprachsynthese | |
DE4111781A1 (de) | Computersystem zur spracherkennung |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A2 Designated state(s): JP US |
|
AL | Designated countries for regional patents |
Kind code of ref document: A2 Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
AK | Designated states |
Kind code of ref document: A3 Designated state(s): JP US |
|
AL | Designated countries for regional patents |
Kind code of ref document: A3 Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2000984858 Country of ref document: EP |
|
WWE | Wipo information: entry into national phase |
Ref document number: 10111695 Country of ref document: US |
|
ENP | Entry into the national phase |
Ref country code: JP Ref document number: 2001 533505 Kind code of ref document: A Format of ref document f/p: F |
|
WWP | Wipo information: published in national office |
Ref document number: 2000984858 Country of ref document: EP |
|
WWG | Wipo information: grant in national office |
Ref document number: 2000984858 Country of ref document: EP |