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    Method and system for semantic language modeling and reliability measurement
    1.
    发明专利
    Method and system for semantic language modeling and reliability measurement 审中-公开
    用于语言语言建模和可靠性测量的方法和系统

    公开(公告)号：JP2005084681A

    公开(公告)日：2005-03-31

    申请号：JP2004254647

    申请日：2004-09-01

    Applicant: Internatl Business Mach Corp ,  インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Maschines Corporation

    Inventor： EPSTEIN MARK E ,  ERDOGAN HAKAN ,  GAO YUQING ,  PICHENY MICHAEL A ,  SARIKAYA RUHI

    IPC: G10L15/18 ,  G10L15/28

    CPC classification number: G10L15/1815

    Abstract: PROBLEM TO BE SOLVED: To provide a system and a method for speech recognition which includes a semantic language model and a phraseological language model and also includes a unification language model as well.
    SOLUTION: The system and method include: generating a set of powerful hypotheses for speech recognition; re-scoring the powerful hypotheses by using semantic contents by using a semantic structured language model; and making the recognized speech articulate by using the semantic structured language model and scoring a syntax analysis tree for identifying the best sentence according to the syntax analysis tree of sentences.
    COPYRIGHT: (C)2005,JPO&NCIPI

    Abstract translation: 要解决的问题：提供一种包括语义语言模型和语言语言模型的语音识别系统和方法，并且还包括统一语言模型。 解决方案：系统和方法包括：产生一组强大的语音识别假设; 通过使用语义结构化语言模型使用语义内容来重新评估强大的假设; 并通过使用语义结构化语言模型来表达公认的语音，并根据句子的语法分析树对用于识别最佳句子的语法分析树进行评分。 版权所有（C）2005，JPO＆NCIPI

    AUTOMATIC GENERATION OF SIMPLE MARKOV MODEL STUNTED BASEFORMS FOR WORDS IN A VOCABULARY
    4.
    发明专利
    AUTOMATIC GENERATION OF SIMPLE MARKOV MODEL STUNTED BASEFORMS FOR WORDS IN A VOCABULARY 未知

    公开(公告)号：CA1238978A

    公开(公告)日：1988-07-05

    申请号：CA504802

    申请日：1986-03-24

    Applicant: IBM

    Inventor： BAHL LALIT R ,  DESOUZA PETER V ,  MERCER ROBERT L ,  PICHENY MICHAEL A

    IPC: G10L15/06 ,  G10L15/14 ,  G10L5/00

    Abstract: AUTOMATIC GENERATION OF SIMPLE MARKOV MODEL STUNTED BASEFORMS FOR WORDS IN A VOCABULARY The present invention addresses the problem of automatically constructing a phonetic-type baseform which, for a given word, is stunted in length relative to a fenemic baseform for the given word. Specifically, in a system that (i) defines each word in a vocabulary by a fenemic baseform of fenemic phones, (ii) defines an alphabet of composite phones each of which corresponds to at least one fenemic phone, and (iii) generates a string of fenemes in response to speech input, the present invention provides for converting a word baseform comprised of fenemic phones into a stunted word baseform of composite phones by (a) replacing each fenemic phone in the fenemic phone word baseform by the composite phone corresponding thereto; and (b) merging together at least one pair of adjacent composite phones by a single composite phone where the adverse effect of the merging is below a predefined threshold.

    SPEECH CODING APPARATUS HAVING SPEAKER DEPENDENT PROTOTYPES GENERATED FROM A NONUSER REFERENCE DATA
    6.
    发明专利
    SPEECH CODING APPARATUS HAVING SPEAKER DEPENDENT PROTOTYPES GENERATED FROM A NONUSER REFERENCE DATA 未知

    公开(公告)号：CA2077728A1

    公开(公告)日：1993-06-06

    申请号：CA2077728

    申请日：1992-09-08

    Applicant: IBM

    Inventor： BAHL LALIT R ,  BELLEGARDA JEROME R ,  DE SOUZA PETER V ,  GOPALAKRISHNAN PONANI S ,  NADAS ARTHUR J ,  NAHAMAOO DAVID ,  PICHENY MICHAEL A

    IPC: G10L19/00 ,  G10L15/02 ,  G10L15/06 ,  G10L15/10

    Abstract: A speech coding apparatus and method for use in a speech recognition apparatus and method. The value of at least one feature of an utterance is measured during each of a series of successive time intervals to produce a series of feature vector signals representing the feature values. A plurality of prototype vector signals, each having at least one parameter value and a unique identification value are stored. The closeness of the feature vector signal is compared to the parameter values of the prototype vector signals to obtain prototype match scores for the feature value signal and each prototype vector signal. The identification value of the prototype vector signal having the best prototype match score is output as a coded representation signal of the feature vector signal. Speaker-dependent prototype vector signals are generated from both synthesized training vector signals and measured training vector signals. The synthesized training vector signals are transformed reference feature vector signals representing the values of features of one or more utterances of one or more speakers in a reference set of speakers. The measured training feature vector signals represent the values of features of one or more utterances of a new speaker/user not in the reference set.

    CONTEXT-DEPENDENT SPEECH RECOGNIZER USING ESTIMATED NEXT WORD CONTEXT
    7.
    发明专利
    CONTEXT-DEPENDENT SPEECH RECOGNIZER USING ESTIMATED NEXT WORD CONTEXT 未知

    公开(公告)号：CA2089786C

    公开(公告)日：1996-12-10

    申请号：CA2089786

    申请日：1993-02-18

    Applicant: IBM

    Inventor： BAHL LALIT R ,  DE SOUZA PETER V ,  GOPALAKRISHNAN PONANI S ,  PICHENY MICHAEL A

    IPC: G10L15/10 ,  G10L15/18 ,  G10L15/28 ,  G10L9/18

    Abstract: A speech recognition apparatus and method estimates the next word context for each current candidate word in a speech hypothesis. An initial model of each speech hypothesis comprises a model of a partial hypothesis of zero or more words followed by a model of a candidate word. An initial hypothesis score for each speech hypothesis comprises an estimate of the closeness of a match between the initial model of the speech hypothesis and a sequence of coded representations of the utterance. The speech hypotheses having the best initial hypothesis scores form an initial subset. For each speech hypothesis in the initial subset, the word which is most likely to follow the speech hypothesis is estimated. A revised model of each speech hypothesis in the initial subset comprises a model of the partial hypothesis followed by a revised model of the candidate word. The revised candidate word model is dependent at least on the word which is estimated to be most likely to follow the speech hypothesis. A revised hypothesis score for each speech hypothesis in the initial subset comprises an estimate of the closeness of a match between the revised model of the speech hypothesis and the sequence of coded representations of the utterance. The speech hypotheses from the initial subset which have the best revised match scores are stored as a reduced subset. At least one word of one or more of the speech hypotheses in the reduced subset is output as a speech recognition result.

    CONTEXT-DEPENDENT SPEECH RECOGNIZER USING ESTIMATED NEXT WORD CONTEXT
    8.
    发明专利
    CONTEXT-DEPENDENT SPEECH RECOGNIZER USING ESTIMATED NEXT WORD CONTEXT 未知

    公开(公告)号：CA2089786A1

    公开(公告)日：1993-10-25

    申请号：CA2089786

    申请日：1993-02-18

    Applicant: IBM

    Inventor： BAHL LALIT R ,  DE SOUZA PETER V ,  GOPALAKRISHNAN PONANI S ,  PICHENY MICHAEL A

    IPC: G10L15/10 ,  G10L15/18 ,  G10L15/28 ,  G10L9/18

    Abstract: A speech recognition apparatus and method estimates the next word context for each current candidate word in a speech hypothesis. An initial model of each speech hypothesis comprises a model of a partial hypothesis of zero or more words followed by a model of a candidate word. An initial hypothesis score for each speech hypothesis comprises an estimate of the closeness of a match between the initial model of the speech hypothesis and a sequence of coded representations of the utterance. The speech hypotheses having the best initial hypothesis scores form an initial subset. For each speech hypothesis in the initial subset, the word which is most likely to follow the speech hypothesis is estimated. A revised model of each speech hypothesis in the initial subset comprises a model of the partial hypothesis followed by a revised model of the candidate word. The revised candidate word model is dependent at least on the word which is estimated to be most likely to follow the speech hypothesis. A revised hypothesis score for each speech hypothesis in the initial subset comprises an estimate of the closeness of a match between the revised model of the speech hypothesis and the sequence of coded representations of the utterance. The speech hypotheses from the initial subset which have the best revised match scores are stored as a reduced subset. At least one word of one or more of the speech hypotheses in the reduced subset is output as a speech recognition result.

    SPEECH RECOGNITION EMPLOYING A SET OF MARKOV MODELS THAT INCLUDES MARKOV MODELS REPRESENTING TRANSITIONS TO AND FROM SILENCE
    9.
    发明专利
    SPEECH RECOGNITION EMPLOYING A SET OF MARKOV MODELS THAT INCLUDES MARKOV MODELS REPRESENTING TRANSITIONS TO AND FROM SILENCE 未知

    公开(公告)号：CA1259411A

    公开(公告)日：1989-09-12

    申请号：CA504807

    申请日：1986-03-24

    Applicant: IBM

    Inventor： BAHL LALIT R ,  DESOUZA PETER V ,  MERCER ROBERT L ,  PICHENY MICHAEL A

    IPC: G10L9/02

    Abstract: SPEECH RECOGNITION EMPLOYING A SET OF MARKOV MODELS THAT INCLUDES MARKOV MODELS REPRESENTING TRANSITIONS TO AND FROM SILENCE The present invention relates to apparatus and method for constructing word baseforms which can be matched against a string of generated acoustic labels which includes: forming a set of phonetic phone machines, wherein each phone machine has (i) a plurality of states, (ii) a plurality of transitions each of which extends from a state to a state, (iii) a stored probability for each transition, and (iv) stored label output probabilities, each label output probability corresponding to the probability of said each phone machine producing a corresponding label; wherein said set of phonetic machines is formed to include a subset of onset phone machines, the stored probabilities of each onset phone machine corresponding to at least one phonetic element being uttered at the beginning of a speech segment; and wherein said set of phonetic machines is formed to include a subset of trailing phone machines, the stored probabilities of each trailing phone machine corresponding to at least one single phonetic element being uttered at the end of a speech segment. Word baseforms are constructed by concatenating phone machines selected from the set.

    FAST ALGORITHM FOR DERIVING ACOUSTIC PROTOTYPES FOR AUTOMATIC SPEECH RECOGNITION
    10.
    发明专利
    FAST ALGORITHM FOR DERIVING ACOUSTIC PROTOTYPES FOR AUTOMATIC SPEECH RECOGNITION 未知

    公开(公告)号：CA2068041C

    公开(公告)日：1996-10-29

    申请号：CA2068041

    申请日：1992-05-05

    Applicant: IBM

    Inventor： BAHL LALIT R ,  BELLEGARDA JEROME R ,  DE SOUZA PETER V ,  NAHAMOO DAVID ,  PICHENY MICHAEL A

    IPC: G10L19/00 ,  G10L15/02 ,  G10L15/06 ,  G10L5/00 ,  G10L5/04 ,  G10L5/06

    Abstract: An apparatus for generating a set of acoustic prototype signals for encoding speech includes means for storing a training script model comprises a series of word-segment models. Each word-segment model comprises a series of elementary models. Means are provided for measuring the value of at least one feature of an utterance of the training script during each of a series of time intervals to produce a series of feature vector signals representing the feature values of the utterance. Means are provided for estimating at least one path through the training script model which would produce the entire series of measured feature vector signals. From the estimated path, the elementary model in the training script model which would produce each feature vector signal is estimated. The apparatus further comprises means for clustering the feature vector signals into a plurality of clusters. Each feature vector signal in a cluster corresponds to a single elementary model in a single location in a single word-segment model. Each cluster signal has a cluster value equal to an average of the feature values of all feature vectors in the signal. Finally, the apparatus includes means for storing a plurality of prototype vector signals. Each prototype vector signal corresponds to an elementary model, has an identifier, and comprises at least two partition values. The partition values are equal to combinations of the cluster values of one or more cluster signals corresponding to the elementary model.

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