EP1097447A1 - Procede et dispositif pour reconnaitre des mots-cles predetermines dans un enonce verbal - Google Patents

Procede et dispositif pour reconnaitre des mots-cles predetermines dans un enonce verbal

Info

Publication number
EP1097447A1
EP1097447A1 EP99945842A EP99945842A EP1097447A1 EP 1097447 A1 EP1097447 A1 EP 1097447A1 EP 99945842 A EP99945842 A EP 99945842A EP 99945842 A EP99945842 A EP 99945842A EP 1097447 A1 EP1097447 A1 EP 1097447A1
Authority
EP
European Patent Office
Prior art keywords
words
filler
keywords
keyword
spoken language
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.)
Withdrawn
Application number
EP99945842A
Other languages
German (de)
English (en)
Inventor
Alfred Hauenstein
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
Siemens Corp
Original Assignee
Siemens AG
Siemens Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Siemens AG, Siemens Corp filed Critical Siemens AG
Publication of EP1097447A1 publication Critical patent/EP1097447A1/fr
Withdrawn legal-status Critical Current

Links

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
    • 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
    • G10L2015/088—Word spotting

Definitions

  • the invention relates to a method and a device for recognizing predetermined keywords in spoken language by a computer.
  • Modeling is understood below to mean the mapping of words into a vocabulary accessible to the system for speech recognition.
  • a vocabulary includes keywords and filler words.
  • a key word is at least one sound that is to be recognized by the system for recognizing spoken language and that is linked in particular to a predetermined action. In particular, a sound contains at least one phoneme.
  • a keyword can also include several words, at least one pause or at least one sound.
  • a noise word denotes an acoustic unit that does not correspond to a keyword, e.g. a word, a sound or a pause.
  • the object of the invention is to provide a method and a device for recognizing keywords in spoken language, in which or in which the disadvantages described above are avoided.
  • Spoken language keywords specified where the keywords are modeled for recognition. Furthermore, a predefined set of filler words is modeled. If a key word occurs in the spoken language, this key word is recognized, otherwise no key word is recognized if a match with a filler word is determined in the spoken language.
  • a further development consists in the fact that the predetermined amount of filler words is small. This is a decisive advantage since the size of the amount of filler words directly influences the computing power of the speech recognition system. A small amount of filler words can also be handled by a computer with relatively low computing power, which is advantageous in terms of the cost of the system for speech recognition. Furthermore, the predetermined amount of filler words is determined from a predetermined number of the most common words in a language.
  • the set of filler words can be the same for all possible combinations of keywords, so that when the keywords are changed, there is no need to change the set of filler words.
  • the filler words are preferably short, monosyllabic words, the acoustic ones
  • Representations match the words of the spoken language that are not keywords, or at least parts of those words.
  • the set of filler words can be obtained from the analysis of spoken dialogues. For this, a list of frequencies in these
  • Words occurring in dialogues are determined and the approx. 15 to 50 most common words selected as filler words.
  • the filler words are preferably provided with a marking. If a keyword matches a filler word from the set of filler words, this filler word is removed from the set of filler words.
  • the keywords and the filler words are then preferably modeled using a system for recognizing spoken language (see [1], [5]). All marked filler words are filtered out of the spoken language and thus only the keywords are displayed to a user or a target application.
  • the determination of the filler words can be based on a statistical analysis of natural spontaneous language. This actually models words spoken by a human and, with the filler words, excellent hit rates for non- Keywords achieved. It is also a particular advantage that the small amount of filler words places little demands on the computing power of the computer to be used.
  • a combination of the invention with known methods for recognizing keywords is also advantageous. This applies in particular to the modeling of noises and pauses (see [2]).
  • Noise word is deleted from the set of noise words if this noise word matches part of a keyword.
  • Another development is that the keywords recognized in the spoken language are displayed and the recognized noise words are not displayed.
  • At least one noise or at least one pause is modeled and added to the set of noise words.
  • One possible use of the method according to the invention is to control a medical device using the key words.
  • Another use of the invention is to answer a customer request, in particular in a communication network, for example the telephone network, the customer request being triggered by a keyword.
  • the system answers a call from a customer who specifies a specific keyword.
  • This enables an automated and efficient interaction of the customer with a computer, whereby a human customer advisor can also be addressed using a keyword.
  • Another development of the invention consists in determining a code word which indicates that a keyword preferably follows immediately. An example is the control of medical devices during the operation with the code word "computer":
  • the code word "computer” signals the system for recognizing key words that a key word "operating table higher” may then be spoken.
  • the code word "computer” can be modeled as a filler word in order not to detect a keyword when the code word is said accidentally without a subsequent keyword.
  • a device for recognizing predetermined keywords in spoken language which has a processor unit which is set up in such a way that the predetermined keywords are modeled for recognition. Furthermore, a predetermined set of filler words is modeled. If a key word occurs in the spoken language, then this key word is recognized, or if a key word is found in the spoken language
  • a further development of the device according to the invention consists in determining the predetermined amount of filler words small or in determining the predetermined amount of filler words from a predetermined number of the most frequent words in a language.
  • This device is particularly suitable for carrying out the method according to the invention or one of its developments explained above. Further developments of the invention also result from the dependent claims.
  • Fig.l a device for recognizing predetermined keywords in spoken language
  • FIG. 2 is a block diagram illustrating a method for recognizing predetermined keywords in spoken language
  • FIG. 3 shows a block diagram which represents a possibility for determining the filler words
  • 5 shows a processor unit
  • speech recognition system generally shows a system architecture for speech recognition (speech recognition system).
  • Speech recognition system comprises several levels of processing.
  • the classification 104 is followed by a search 105 for predefined filler words 106, application-specific keywords 107 or predefined noise models 108 (optionally, it is also possible to model pauses).
  • the assignments 106, 107 and / or 108 made on the basis of the search 105 are filtered in a logical block 109 and the sequence of found keywords 110 is output.
  • FIG. 2 shows a block diagram illustrating a method for recognizing predetermined keywords in spoken language.
  • the keywords are modeled in a step 201.
  • the filler words are modeled.
  • the components of the spoken language sounds
  • the keywords found are displayed in a step 204.
  • the spoken language 301 is broken down into sounds (components) and these sounds are sorted according to their frequency (see step 302).
  • a sound 304 is particularly a word 305, a syllable 306, multiple words 307, a sound 308 or a pause 309.
  • Fig. 4 shows a list of possible filler words.
  • the filler words are common in natural language dialogues in the modeled language (e.g. German) and are ideal for modeling non-key words.
  • Fig. 4 shows an example of a list with 1! Fillers:
  • a computing unit 501 is shown in FIG.
  • the computing unit 501 comprises a processor CPU 502, one
  • the computing unit 501 also has a bus 506, which ensures the connection of memory 503, processor 502 and input / output interface 504. It is also possible to connect additional components to bus 506: additional memory, hard disk, etc. Via interface 505 or bus 506, it is possible to control external devices or another program running on another computer.
  • the following publications have been cited in this document:

Landscapes

  • Engineering & Computer Science (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 et un dispositif pour reconnaître des mots-clés prédéterminés dans un énoncé verbal. Selon l'invention, les mots-clés sont modélisés pour la reconnaissance. Un nombre prédéterminé d'explétifs est en outre modélisé. Lorsqu'un mot-clé apparaît dans l'énoncé verbal, il est reconnu. En revanche, aucun mot-clé n'est reconnu lorsqu'une coïncidence avec un explétif est déterminée dans l'énoncé verbal.
EP99945842A 1998-07-23 1999-07-01 Procede et dispositif pour reconnaitre des mots-cles predetermines dans un enonce verbal Withdrawn EP1097447A1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE19833212 1998-07-23
DE19833212 1998-07-23
PCT/DE1999/001971 WO2000005709A1 (fr) 1998-07-23 1999-07-01 Procede et dispositif pour reconnaitre des mots-cles predetermines dans un enonce verbal

Publications (1)

Publication Number Publication Date
EP1097447A1 true EP1097447A1 (fr) 2001-05-09

Family

ID=7875090

Family Applications (1)

Application Number Title Priority Date Filing Date
EP99945842A Withdrawn EP1097447A1 (fr) 1998-07-23 1999-07-01 Procede et dispositif pour reconnaitre des mots-cles predetermines dans un enonce verbal

Country Status (3)

Country Link
US (1) US20010016814A1 (fr)
EP (1) EP1097447A1 (fr)
WO (1) WO2000005709A1 (fr)

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US8355912B1 (en) * 2000-05-04 2013-01-15 International Business Machines Corporation Technique for providing continuous speech recognition as an alternate input device to limited processing power devices
GB0027830D0 (en) * 2000-11-14 2000-12-27 Calder Robert M Anti social behaviour
US7797159B2 (en) * 2002-09-16 2010-09-14 Movius Interactive Corporation Integrated voice navigation system and method
US7761321B2 (en) * 2006-02-22 2010-07-20 24/7 Customer, Inc. System and method for customer requests and contact management
US9129290B2 (en) * 2006-02-22 2015-09-08 24/7 Customer, Inc. Apparatus and method for predicting customer behavior
US8396741B2 (en) 2006-02-22 2013-03-12 24/7 Customer, Inc. Mining interactions to manage customer experience throughout a customer service lifecycle
US7752152B2 (en) * 2006-03-17 2010-07-06 Microsoft Corporation Using predictive user models for language modeling on a personal device with user behavior models based on statistical modeling
US8032375B2 (en) * 2006-03-17 2011-10-04 Microsoft Corporation Using generic predictive models for slot values in language modeling
US7689420B2 (en) * 2006-04-06 2010-03-30 Microsoft Corporation Personalizing a context-free grammar using a dictation language model
US8370127B2 (en) * 2006-06-16 2013-02-05 Nuance Communications, Inc. Systems and methods for building asset based natural language call routing application with limited resources
EP2608196B1 (fr) * 2011-12-21 2014-07-16 Institut Telecom - Telecom Paristech Procédé combinatoire pour la génération de mots explétifs
CN103971678B (zh) * 2013-01-29 2015-08-12 腾讯科技（深圳）有限公司 关键词检测方法和装置
US9892729B2 (en) * 2013-05-07 2018-02-13 Qualcomm Incorporated Method and apparatus for controlling voice activation
US9747899B2 (en) * 2013-06-27 2017-08-29 Amazon Technologies, Inc. Detecting self-generated wake expressions
US10311874B2 (en) 2017-09-01 2019-06-04 4Q Catalyst, LLC Methods and systems for voice-based programming of a voice-controlled device
CN109994106B (zh) * 2017-12-29 2023-06-23 阿里巴巴集团控股有限公司 一种语音处理方法及设备
US12494205B2 (en) * 2023-01-06 2025-12-09 Toyota Connected North America, Inc. Data structure for task-oriented dialog modeling

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US5509104A (en) * 1989-05-17 1996-04-16 At&T Corp. Speech recognition employing key word modeling and non-key word modeling
US6463361B1 (en) * 1994-09-22 2002-10-08 Computer Motion, Inc. Speech interface for an automated endoscopic system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO0005709A1 *

Also Published As

Publication number Publication date
WO2000005709A1 (fr) 2000-02-03
US20010016814A1 (en) 2001-08-23

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