EP1518221A1 - Procede de reconnaissance vocale naturelle fondee sur une grammaire syntagmatique/generative transformationnelle - Google Patents
Procede de reconnaissance vocale naturelle fondee sur une grammaire syntagmatique/generative transformationnelleInfo
- Publication number
- EP1518221A1 EP1518221A1 EP03761435A EP03761435A EP1518221A1 EP 1518221 A1 EP1518221 A1 EP 1518221A1 EP 03761435 A EP03761435 A EP 03761435A EP 03761435 A EP03761435 A EP 03761435A EP 1518221 A1 EP1518221 A1 EP 1518221A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- grammar
- recognized
- words
- phrase
- word
- Prior art date
- 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.)
- Ceased
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000009466 transformation Effects 0.000 title claims abstract description 8
- 230000015572 biosynthetic process Effects 0.000 claims description 4
- 230000001755 vocal effect Effects 0.000 claims description 4
- 230000009471 action Effects 0.000 claims description 3
- 230000000875 corresponding effect Effects 0.000 claims 2
- 230000001960 triggered effect Effects 0.000 claims 1
- 230000008569 process Effects 0.000 description 7
- 241000282312 Proteles Species 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000014509 gene expression Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000009897 systematic effect Effects 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
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
- G10L15/19—Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence 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
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1815—Semantic context, e.g. disambiguation of the recognition hypotheses based on word meaning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99934—Query formulation, input preparation, or translation
Definitions
- the invention relates to a method for natural speech recognition based on a generative transformation / phrase structure grammar (GT / PS grammar).
- NLU Natural Language Understanding
- Speech recognition systems with natural speech recognition are able to understand a variety of possible utterances and implement them in complex command structures, the speech recognition systems, e.g. Computer, to take certain actions. They do this on the basis of predefined, meaningful sample sets, which are defined by application developers and so-called dialog designers.
- This collection of sample sentences - also called "grammar" - includes individual command words as well as complicated nesting sentences that make sense at a certain point in the dialog. If the user expresses such a sentence, the system will understand it with great certainty and the instructions associated with it is running.
- the Grammar is an indispensable component. It is generated using a special tool, the so-called Grammar Specification Language (GSL). It is used to reproduce the words to be understood as well as their links in advance and to lay them down for the speech recognizer.
- GSL Grammar Specification Language
- the predefined sentences are formed from combinations of words that are interchangeable (paradigmatic axis) and combinable (syntagmatic axis). An example of this is shown in FIG. 7. The possible utterances result from the syntagmatic connection of the paradigmatic word combinations.
- the object of the invention is to provide a method for speech recognition on the basis of a generative transformation / phrase structure grammar which, compared to conventional recognition methods, requires less system resources and thereby enables reliable and fast recognition of speech while reducing over-generation.
- a spoken phrase is analyzed for triphones contained therein, words contained in the spoken phrase are formed from the recognized triphones with the aid of phonetic word databases
- the linking rules of grammatical sentences are not reproduced on the surface, but the depth structures are shown, which are followed by the syntagmatic links of all Indo-European languages.
- Each sentence is described using a syntactic model in the form of so-called structure trees.
- the GT / PS grammar is not based on the potential statements of a specific application, but on the deep structure of the syntax (sentence formation rules) of Indo-European languages. It provides a framework that can be filled with different words and depicts the reality of the spoken language better than the previously used "mimetic" process.
- Subgrammars in the GT / PS model on e.g. 30 subgrammars can be reduced in just two hierarchical levels.
- the new grammar type depicts natural language expressions in a structured form and is only around 25% the size of the previous grammar, for example. Because of its small size, this grammar is easier to maintain, and the times for compilation decrease rapidly. Due to their small size, the Detection reliability (Accuracy) and decreases the detection delay (Latency). Current computer capacities are better used and the performance of the servers increases. In addition, the new Grammar is not related to a specific application, but can be used in its basic structures for different applications, which increases the homogeneity of the systems and reduces development times.
- the universal code of the deep structure enables the use and added value for multilingual language systems in a dimension that has not yet been achieved, especially the standard Western European languages can be processed with comparatively little effort.
- the new GT / PS grammar is based on current linguistic models that provide natural-language utterances in the context of surface and
- GSL Grammar Specification Language
- the GT / PS grammar is much smaller than the previous grammar because it only needs two levels instead of the up to seven subgrammar levels; - The number of grammatically incorrect sentences covered by the grammar
- Figure 1 A triphone analysis as the first step in the recognition process
- Figure 2 Word recognition from the recognized triphones as a second step in the recognition process
- Figure 3 a syntactic reconstruction of the recognized words as the third step of the recognition process
- Figure 4 An example of the structure of the recognized words in
- Figure 5 A sample program for a possible grammar
- Figure 6 An overview of the structure of a PSG grammar
- Figure 7 An example of the formation of word combinations in a grammar according to the prior art.
- Figure 1 shows the first step of speech recognition: the triphone analysis.
- the continuous flow of speech of a person 1 is e.g. accepted by a microphone of a telephone and fed to a speech recognizer 2 as an analog signal.
- the analog voice signal is converted into a digital voice signal 3.
- the speech signal contains a variety of triphones, i.e. Sound segments that in speech recognizer 2 with existing, i.e. Predefined triphon linking rules are compared.
- the existing triphones are stored in a database which contains one or more phonebooks.
- the recognized triphones are then present as a triphone chain 4, e.g. "Pro", “red”, “ote", "tel”.
- useful words are formed from the recognized triphones.
- the phonetic dictionary 5 can comprise a certain vocabulary from the colloquial language as well as a special vocabulary tailored to the respective application.
- the recognized words 7 are reconstructed using the grammar 8.
- the recognized words are assigned to their part of speech, such as noun, verb, adverb, article, adjective, etc., as shown in FIG 6 is shown.
- the databases 9-15 can contain both the conventional part of speech categories mentioned above and special part of speech types, such as yes / no grammar 9, telephone numbers 14, 15.
- a detection of DTMF inputs 16 can also be provided.
- the described assignment of the part of speech type to the recognized words can already take place during the word recognition process.
- the recognized words are based on their word categories of a verbal phrase, i.e. a verb-based phrase, and a nominal phrase, i.e. assigned to a phrase based on a noun, cf. Figure 6.
- step 18 the objects for multitasking are linked to the corresponding voice-controlled application.
- Each object 19 comprises a target sentence stored in the grammar 8, more precisely a sentence model.
- a sentence model e.g. can be defined by a word order "subject, verb, object” or "object, verb, subject”.
- Many other sentence structures are stored in this general form in Grammar 8. If the word categories of the recognized words 7 correspond to the order of one of the predefined sentence models, they are assigned to the associated object. The sentence is considered recognized. In other words, each sentence model comprises a number of variables assigned to the different word categories, which are filled with the corresponding word categories of the recognized words 7.
- the procedure uses the traditional Grammar Specification Language (GSL), but structures the stored sentences in an innovative way. It is based on the rules of phrase structure grammar and the concept of a generative transformation grammar.
- GSL Grammar Specification Language
- the GT / PS grammar is therefore based on a theoretical model that is suitable for determining the abstract principles of natural language utterances.
- it opens up the possibility for the first time to reverse the abstraction of sentence formation rules and to substantiate them as a prediction of the statements made by application users. This enables systematic access to speech recognition grammars that have always been based on the intuitive accumulation of example sentences.
- a central feature of conventional and GT / PS grammars is the hierarchical nesting into so-called subgrammars, which combine individual words and variables at the highest level to form an entire sentence.
- the GT / PS grammar is much smaller and hierarchically much clearer than the previously known grammars.
- "meaningful" sentences are almost exclusively stored in the new grammar, so that the degree of overgeneration, ie stored sentences that are incorrect in the natural language sense, decreases. This, in turn, is the prerequisite for improved recognition performance, since the Application only has to choose between a few stored alternatives.
Landscapes
- Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (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)
Abstract
L'invention concerne un procédé de reconnaissance vocale naturelle fondée sur une grammaire syntagmatique/générative transformationnelle. Selon l'invention, une analyse d'une phrase prononcée est réalisée pour permettre de déterminer les triphones contenus dans cette dernière, puis des mots contenus dans la phrase prononcée sont formés à partir des triphones reconnus, à l'aide de bases de données de groupes de phonèmes (dictionnaires) et une reconstruction syntaxique de la phrase prononcée est réalisée à partir des mots reconnus au moyen d'une ensemble de règles grammaticales (grammaire). Cette grammaire syntagmatique/générative transformationnelle constitue un nouveau procédé pour mémoriser des phrases cibles dans la grammaire. Elle fait appel à la GSL ( </= Grammar Specification Language >/= ), mais structure les phrases à mémoriser de manière novatrice. Elle se fonde sur les règles de la grammaire syntagmatique et sur le concept de grammaire générative transformationnelle de Noam Chomsky.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE10229207A DE10229207B3 (de) | 2002-06-28 | 2002-06-28 | Verfahren zur natürlichen Spracherkennung auf Basis einer Generativen Transformations-/Phrasenstruktur-Grammatik |
DE10229207 | 2002-06-28 | ||
PCT/DE2003/002135 WO2004003888A1 (fr) | 2002-06-28 | 2003-06-26 | Procede de reconnaissance vocale naturelle fondee sur une grammaire syntagmatique/generative transformationnelle |
Publications (1)
Publication Number | Publication Date |
---|---|
EP1518221A1 true EP1518221A1 (fr) | 2005-03-30 |
Family
ID=29795990
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP03761435A Ceased EP1518221A1 (fr) | 2002-06-28 | 2003-06-26 | Procede de reconnaissance vocale naturelle fondee sur une grammaire syntagmatique/generative transformationnelle |
Country Status (10)
Country | Link |
---|---|
US (1) | US7548857B2 (fr) |
EP (1) | EP1518221A1 (fr) |
JP (1) | JP4649207B2 (fr) |
CN (1) | CN1315109C (fr) |
AU (1) | AU2003250272A1 (fr) |
CA (1) | CA2493429C (fr) |
DE (1) | DE10229207B3 (fr) |
IL (1) | IL165957A (fr) |
PL (1) | PL373306A1 (fr) |
WO (1) | WO2004003888A1 (fr) |
Families Citing this family (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7295981B1 (en) * | 2004-01-09 | 2007-11-13 | At&T Corp. | Method for building a natural language understanding model for a spoken dialog system |
GB0517082D0 (en) | 2005-08-19 | 2005-09-28 | Univ City Hong Kong | Auxiliary winding for improved performance of a planar inductive charging platform |
EP2141692A1 (fr) | 2008-06-26 | 2010-01-06 | Deutsche Telekom AG | Assistance automatisée à commande vocale d'un utilisateur |
KR101195812B1 (ko) * | 2010-07-08 | 2012-11-05 | 뷰모션 (주) | 규칙기반 시스템을 이용한 음성인식 시스템 및 그 방법 |
US9817813B2 (en) * | 2014-01-08 | 2017-11-14 | Genesys Telecommunications Laboratories, Inc. | Generalized phrases in automatic speech recognition systems |
CN110164449B (zh) * | 2019-04-26 | 2021-09-24 | 安徽美博智能科技有限公司 | 语音识别的空调机控制方法及装置 |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
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WO1998009228A1 (fr) * | 1996-08-29 | 1998-03-05 | Bcl Computers, Inc. | Procede de commande vocale en langage naturel |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
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DE3786822T2 (de) * | 1986-04-25 | 1994-01-13 | Texas Instruments Inc | Spracherkennungssystem. |
EP0590173A1 (fr) * | 1992-09-28 | 1994-04-06 | International Business Machines Corporation | Système de reconnaissance de la parole utilisant un ordinateur |
JPH0769710B2 (ja) * | 1993-03-23 | 1995-07-31 | 株式会社エイ・ティ・アール自動翻訳電話研究所 | 自然言語解析方法 |
US6070140A (en) * | 1995-06-05 | 2000-05-30 | Tran; Bao Q. | Speech recognizer |
US6182039B1 (en) * | 1998-03-24 | 2001-01-30 | Matsushita Electric Industrial Co., Ltd. | Method and apparatus using probabilistic language model based on confusable sets for speech recognition |
US6163768A (en) * | 1998-06-15 | 2000-12-19 | Dragon Systems, Inc. | Non-interactive enrollment in speech recognition |
JP2950823B1 (ja) * | 1998-09-29 | 1999-09-20 | 株式会社エイ・ティ・アール音声翻訳通信研究所 | 音声認識誤り訂正装置 |
JP3581044B2 (ja) * | 1999-05-20 | 2004-10-27 | 株式会社東芝 | 音声対話処理方法、音声対話処理システムおよびプログラムを記憶した記憶媒体 |
US7120582B1 (en) * | 1999-09-07 | 2006-10-10 | Dragon Systems, Inc. | Expanding an effective vocabulary of a speech recognition system |
US6633846B1 (en) * | 1999-11-12 | 2003-10-14 | Phoenix Solutions, Inc. | Distributed realtime speech recognition system |
DE10032255A1 (de) * | 2000-07-03 | 2002-01-31 | Siemens Ag | Verfahren zur Sprachanalyse |
US7058567B2 (en) * | 2001-10-10 | 2006-06-06 | Xerox Corporation | Natural language parser |
-
2002
- 2002-06-28 DE DE10229207A patent/DE10229207B3/de not_active Expired - Lifetime
-
2003
- 2003-06-26 US US10/519,653 patent/US7548857B2/en not_active Expired - Fee Related
- 2003-06-26 CN CNB038152843A patent/CN1315109C/zh not_active Expired - Fee Related
- 2003-06-26 EP EP03761435A patent/EP1518221A1/fr not_active Ceased
- 2003-06-26 AU AU2003250272A patent/AU2003250272A1/en not_active Abandoned
- 2003-06-26 PL PL03373306A patent/PL373306A1/xx not_active Application Discontinuation
- 2003-06-26 JP JP2004516499A patent/JP4649207B2/ja not_active Expired - Fee Related
- 2003-06-26 CA CA2493429A patent/CA2493429C/fr not_active Expired - Fee Related
- 2003-06-26 WO PCT/DE2003/002135 patent/WO2004003888A1/fr active Application Filing
-
2004
- 2004-12-23 IL IL165957A patent/IL165957A/en active IP Right Grant
Patent Citations (1)
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WO1998009228A1 (fr) * | 1996-08-29 | 1998-03-05 | Bcl Computers, Inc. | Procede de commande vocale en langage naturel |
Non-Patent Citations (7)
Title |
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FRÉDÉRIC BÉCHET ET AL: "LARGE SPAN STATISTICAL LANGUAGE MODELS : APPLICATION TO HOMOPHONE DISAMBIGUATION FOR LARGE VOCABULARY SPEECH RECOGNITION IN FRENCH", CONFERENCE PROCEEDINGS ON CD-ROM, EUROSPEECH'99, 6TH EUROPEAN CONFERENCE ON SPEECH COMMUNICATION AND TECHNOLOGY,, vol. 4, 5 September 1999 (1999-09-05) - 9 September 1999 (1999-09-09), Budapest, Hungary, pages 1763, XP007001340, ISSN: 1018-4074 * |
I. ZITOUNI, K. SMAÏLI, J.P. HATON: "STATISTICAL LANGUAGE MODEL BASEDON A HIERARCHICAL APPROACH:MC", 7TH EUROPEAN CONFERENCE ON SPEECH COMMUNICATION AND TECHNOLOGY, AALBORG, DENMARK, 2001, 7 September 2001 (2001-09-07), Aalborg, Denmark, 2001, Retrieved from the Internet <URL:http://www.loria.fr/%7Esmaili/Euro01.pdf> [retrieved on 20110204] * |
IMED ZITOUNI: "Modélisation du langage pour les systèmes de reconnaissance de la parole destinés aux grands vocabulaires : application à MAUD", DISSERTATION, 31 March 2000 (2000-03-31), UNIVERSITÉ HENRI POINCARÉ - NANCY, XP055005773, Retrieved from the Internet <URL:http://www.afcp-parole.org/doc/theses/theseIZ00.ps.gz> [retrieved on 20110204] * |
IMED ZITOUNI: "Modélisation du langage pour les systèmes de reconnaissance de la parole destinés aux grands vocabulaires : application à MAUD", DISSERTATION, 7 September 2001 (2001-09-07), UNIVERSITÉ HENRI POINCARÉ - NANCY, pages 1 - 188, XP055005773 * |
IMED ZITOUNI: "Modélisation du langage pour les systèmes de reconnaissance de la parole destinés aux grands vocabulaires : application à MAUD", RESUMES DE THESES, INFORMATION IN COGNITO, vol. 20, 31 December 2001 (2001-12-31), nancy, Retrieved from the Internet <URL:http://www.in-cognito.net/new/images/article/zitouni20.pdf> [retrieved on 20110204] * |
M. HASPELMATH: "Word classes and parts of speech", 31 December 2001 (2001-12-31), pages 16538 - 16545, ISBN: 0-08-043076-7, Retrieved from the Internet <URL:www.eva.mpg.de/~haspelmt/2001wcl.pdf> [retrieved on 20110204] * |
See also references of WO2004003888A1 * |
Also Published As
Publication number | Publication date |
---|---|
JP2005539249A (ja) | 2005-12-22 |
US20060161436A1 (en) | 2006-07-20 |
CN1666254A (zh) | 2005-09-07 |
IL165957A (en) | 2010-11-30 |
JP4649207B2 (ja) | 2011-03-09 |
CN1315109C (zh) | 2007-05-09 |
CA2493429C (fr) | 2011-09-13 |
WO2004003888B1 (fr) | 2004-03-25 |
IL165957A0 (en) | 2006-01-15 |
PL373306A1 (en) | 2005-08-22 |
US7548857B2 (en) | 2009-06-16 |
DE10229207B3 (de) | 2004-02-05 |
CA2493429A1 (fr) | 2004-01-08 |
AU2003250272A1 (en) | 2004-01-19 |
WO2004003888A1 (fr) | 2004-01-08 |
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