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公开(公告)号：CA2060591C
公开(公告)日：1996-08-13
申请号：CA2060591
申请日：1992-02-04
Applicant: IBM
Inventor： BAHL LALIT R , PICHENY MICHAEL A , NAHAMOO DAVID , DE SOUZA PETER V
Abstract: The present invention is related to speech recognition and particularly to a new type of vector quantizer and a new vector quantization technique in which the error rate of associating a sound with an incoming speech signal is drastically reduced. To achieve this end, the present invention technique groups the feature vectors in a space into different prototypes at least two of which represent a class of sound. Each of the prototypes may in turn have a number of subclasses or partitions. Each of the prototypes and their subclasses may be assigned respective identifying values. To identify an incoming speech feature vector, at least one of the feature values of the incoming feature vector is compared with the different values of the respective prototypes, or the subclasses of the prototypes. The class of sound whose group of prototypes, or at least one of the prototypes, whose combined value most closely matches the value of the feature value of the feature vector is deemed to be the class corresponding to the feature vector. The feature vector is then labeled with the identifier associated with that class.
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12.发明专利
公开(公告)号：CA2077728C
公开(公告)日：1996-08-06
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 , NAHAMOO DAVID , PICHENY MICHAEL A
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.
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公开(公告)号：CA2073991A1
公开(公告)日：1993-04-24
申请号：CA2073991
申请日：1992-07-16
Applicant: IBM
Inventor： BAHL LALIT R , DE SOUZA PETER V , GOPALAKRISHNAM PONANI S , PICHENY MICHAEL A
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公开(公告)号：CA1241751A
公开(公告)日：1988-09-06
申请号：CA504801
申请日：1986-03-24
Applicant: IBM
Inventor： BAHL LALIT R , DESOUZA PETER V , MERCER ROBERT L , PICHENY MICHAEL A
Abstract: The present invention addresses the problem of constructing fenemic baseforms which take into account variations in pronunciation of words from one utterance thereof to another. Specifically, the invention relates to a method of constructing a fenemic baseform for a word in a vocabulary of word segments including the steps of: (a) transforming multiple utterances of the word into respective strings of fenemes; (b) defining a set of fenemic Markov model phone machines; (c) determining the best single phone machine P1 for producing the multiple feneme strings; (d) determining the best two phone baseform of the form P1P2 or P2P1 for producing the multiple feneme strings; (e) aligning the best two phone baseform against each feneme string; (f) splitting each feneme string into a left portion and a right portion with the left portion corresponding to the first phone machine of the two phone baseform and the right portion corresponding to the second phone machine of the two phone baseform; (g) identifying each left portion as a left substring and each right portion as a right substring; (h) processing the set of left substrings and the set of right substrings in the same manner as the set of feneme strings corresponding to the multiple utterances including the further step of inhibiting further splitting of a substring when the single phone baseform thereof has a higher probability of producing the substring than does the best two phone baseform; and (k) concatenating the unsplit single phones in an order corresponding to the order of the feneme substrings to which they correspond.
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公开(公告)号：DE69422466T2
公开(公告)日：2000-07-06
申请号：DE69422466
申请日：1994-09-07
Applicant: IBM
Inventor： ELLOZY HAMED A , KANEVSKY DIMITRI , KIM MICHELLE Y , NAHAMOO DAVID , PICHENY MICHAEL A , ZADROZNY WLODEK W
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Patent Agency Ranking
公开(公告)号：DE69422466D1
公开(公告)日：2000-02-10
申请号：DE69422466
申请日：1994-09-07
Applicant: IBM
Inventor： ELLOZY HAMED A , KANEVSKY DIMITRI , KIM MICHELLE Y , NAHAMOO DAVID , PICHENY MICHAEL A , ZADROZNY WLODEK W
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17.发明专利
公开(公告)号：CA2072721A1
公开(公告)日：1993-04-04
申请号：CA2072721
申请日：1992-06-29
Applicant: IBM
Inventor： BAHL LALIT R , BELLEGARDA JEROME R , EPSTEIN EDWARD A , LUCASSEN JOHN M , NAHAMOO DAVID , PICHENY MICHAEL A
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公开(公告)号：CA2068041A1
公开(公告)日：1993-01-17
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
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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公开(公告)号：CA2060591A1
公开(公告)日：1992-09-23
申请号：CA2060591
申请日：1992-02-04
Applicant: IBM
Inventor： BAHL LALIT R , PICHENY MICHAEL A , NAHAMOO DAVID , DE SOUZA PETER V
Abstract: The present invention is related to speech recognition and particularly to a new type of vector quantizer and a new vector quantization technique in which the error rate of associating a sound with an incoming speech signal is drastically reduced. To achieve this end, the present invention technique groups the feature vectors in a space into different prototypes at least two of which represent a class of sound. Each of the prototypes may in turn have a number of subclasses or partitions. Each of the prototypes and their subclasses may be assigned respective identifying values. To identify an incoming speech feature vector, at least one of the feature values of the incoming feature vector is compared with the different values of the respective prototypes, or the subclasses of the prototypes. The class of sound whose group of prototypes, or at least one of the prototypes, whose combined value most closely matches the value of the feature value of the feature vector is deemed to be the class corresponding to the feature vector. The feature vector is then labeled with the identifier associated with that class.
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公开(公告)号：CA1236578A
公开(公告)日：1988-05-10
申请号：CA496161
申请日：1985-11-26
Applicant: IBM
Inventor： BAHL LALIT R , DESOUZA PETER V , MERCER ROBERT L , PICHENY MICHAEL A
IPC: G10L5/00
Abstract: FENEME-BASED MARKOV MODELS FOR WORDS In a speech recognition system, apparatus and method for modelling words with label-based Markov models is disclosed. The modelling includes: entering a first speech input, corresponding to words in a vocabulary, into an acoustic processor which converts each spoken word into a sequence of standard labels, where each standard label corresponds to a sound type assignable to an interval of time; representing each standard label as a probabilistic model which has a plurality of states, at least one transition from a state to a state, and at least one settable output probability at some transitions; entering selected acoustic inputs into an acoustic processor which converts the selected acoustic inputs into personalized labels, each personalized label corresponding to a sound type assigned to an interval of time; and setting each output probability as the probability or the standard label represented by a given model producing a particular personalized label at a given transition in the given model. The present invention addresses the problem of generating models of words simply and automatically in a speech recognition system.
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