公开(公告)号：US5278942A

公开(公告)日：1994-01-11

申请号：US802678

申请日：1991-12-05

申请人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Arthur J. Nadas ,  David Nahamoo ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Arthur J. Nadas ,  David Nahamoo ,  Michael A. Picheny

IPC分类号： G10L19/00 ,  G10L15/02 ,  G10L15/06 ,  G10L15/10 ,  G10L9/02

CPC分类号： G10L15/063 ,  G10L15/02

摘要： 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.

摘要翻译： 一种用于语音识别装置和方法的语音编码装置和方法。 在一系列连续时间间隔的每一个期间测量话音的至少一个特征的值，以产生表示特征值的一系列特征向量信号。 存储多个具有至少一个参数值和唯一识别值的原型矢量信号。 将特征矢量信号的接近度与原型矢量信号的参数值进行比较，以获得特征值信号和每个原型矢量信号的原型匹配分数。 输出具有最佳原型匹配分数的原型矢量信号的识别值作为特征矢量信号的编码表示信号。 从合成的训练矢量信号和测量的训练矢量信号产生与扬声器相关的原型矢量信号。 合成的训练矢量信号是变换的参考特征矢量信号，其代表参考的一组扬声器中的一个或多个扬声器的一个或多个话音的特征值。 测量的训练特征向量信号表示不在参考集合中的新的说话者/用户的一个或多个话语的特征值。

公开(公告)号：US5129001A

公开(公告)日：1992-07-07

申请号：US514075

申请日：1990-04-25

申请人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  David Nahamoo ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  David Nahamoo ,  Michael A. Picheny

IPC分类号： G06F7/00 ,  G06F17/18 ,  G10L15/06 ,  G10L15/10 ,  G10L15/14

CPC分类号： G10L15/144

摘要： Modeling a word is done by concatenating a series of elemental models to form a word model. At least one elemental model in the series is a composite elemental model formed by combining the starting states of at least first and second primitive elemental models. Each primitive elemental model represents a speech component. The primitive elemental models are combined by a weighted combination of their parameters in proportion to the values of the weighting factors. To tailor the word model to closely represent variations in the pronunciation of the word, the word is uttered a plurality of times by a plurality of different speakers. Constructing word models from composite elemental models, and constructing composite elemental models from primitive elemental models enables word models to represent many variations in the pronunciation of a word. Providing a relatively small set of primitive elemental models for a relatively large vocabulary of words enables models to be trained to the voice of a new speaker by having the new speaker utter only a small subset of the words in the vocabulary.

公开(公告)号：US5195167A

公开(公告)日：1993-03-16

申请号：US871600

申请日：1992-04-17

申请人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  David Nahamoo ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  David Nahamoo ,  Michael A. Picheny

IPC分类号： G06F7/38 ,  G06F17/27 ,  G10L11/00 ,  G10L15/02 ,  G10L15/06 ,  G10L15/10 ,  G10L15/18

CPC分类号： G10L15/063

摘要： Symbol feature values and contextual feature values of each event in a training set of events are measured. At least two pairs of complementary subsets of observed events are selected. In each pair of complementary subsets of observed events, one subset has contextual features with values in a set C.sub.n, and the other set has contextual features with values in a set C.sub.n, were the sets in C.sub.n and C.sub.n are complementary sets of contextual feature values. For each subset of observed events, the similarity values of the symbol features of the observed events in the subsets are calculated. For each pair of complementary sets of observed events, a "goodness of fit" is the sum of the symbol feature value similarity of the subsets. The sets of contextual feature values associated with the subsets of observed events having the best "goodness of fit" are identified and form context-dependent bases for grouping the observed events into two output sets.

摘要翻译： 测量训练集中的每个事件的符号特征值和上下文特征值。 选择观察事件的至少两对互补子集。 在观察事件的每对互补子集中，一个子集具有集合C n中的值的上下文特征，另一个集合具有集合Cn中的值的上下文特征，Cn和Cn中的集合是上下文特征值的互补集合 。 对于观察事件的每个子集，计算子集中观察事件的符号特征的相似度值。 对于每对观察事件的互补集合，“拟合优度”是子集的符号特征值相似度的总和。 识别与具有最佳“拟合优度”的观察事件的子集相关联的上下文特征值集合，并形成用于将观察到的事件分组为两个输出集合的上下文相关基础。

公开(公告)号：US5333236A

公开(公告)日：1994-07-26

申请号：US942862

申请日：1992-09-10

申请人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

IPC分类号： G10L15/10 ,  G10L15/14 ,  G10L15/18 ,  G10L19/00 ,  G10L19/04 ,  G10L19/06 ,  G10L19/08 ,  G10L9/00

CPC分类号： G10L19/06

摘要： A speech coding apparatus compares the closeness of the feature value of a feature vector signal of an utterance to the parameter values of prototype vector signals to obtain prototype match scores for the feature vector signal and each prototype vector signal. The speech coding apparatus stores a plurality of speech transition models representing speech transitions. At least one speech transition is represented by a plurality of different models. Each speech transition model has a plurality of model outputs, each comprising a prototype match score for a prototype vector signal. Each model output has an output probability. A model match score for a first feature vector signal and each speech transition model comprises the output probability for at least one prototype match score for the first feature vector signal and a prototype vector signal. A speech transition match score for the first feature vector signal and each speech transition comprises the best model match score for the first feature vector signal and all speech transition models representing the speech transition. The identification value of each speech transition and the speech transition match score for the first feature vector signal and each speech transition are output as a coded utterance representation signal of the first feature vector signal.

摘要翻译： 语音编码装置将发声特征矢量信号的特征值与原型矢量信号的参数值的接近度进行比较，以获得特征向量信号和每个原型矢量信号的原型匹配分数。 语音编码装置存储表示语音转换的多个语音转换模型。 至少一个语音转换由多个不同的模型表示。 每个语音转换模型具有多个模型输出，每个模型输出包括原型矢量信号的原型匹配分数。 每个模型输出具有输出概率。 用于第一特征向量信号和每个语音转换模型的模型匹配分数包括用于第一特征向量信号和原型矢量信号的至少一个原型匹配分数的输出概率。 用于第一特征向量信号和每个语音转换的语音转换匹配分数包括用于第一特征向量信号的最佳模型匹配分数和表示语音转换的所有语音转换模型。 输出第一特征矢量信号和每个语音转换的每个语音转换的识别值和语音转换匹配分数作为第一特征向量信号的编码话音表示信号。

公开(公告)号：US5233681A

公开(公告)日：1993-08-03

申请号：US874271

申请日：1992-04-24

申请人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

IPC分类号： G10L15/10 ,  G10L15/18 ,  G10L15/28

CPC分类号： G10L15/19 ,  G10L15/193

摘要： 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.

公开(公告)号：US5182773A

公开(公告)日：1993-01-26

申请号：US673810

申请日：1991-03-22

申请人： Lalit R. Bahl ,  Michael A. Picheny ,  David Nahamoo ,  Peter V. de Souza

发明人： Lalit R. Bahl ,  Michael A. Picheny ,  David Nahamoo ,  Peter V. de Souza

IPC分类号： G10L19/00 ,  G10L15/02 ,  G10L19/02 ,  H03M7/30

CPC分类号： H03M7/3082 ,  G10L19/038

摘要： 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.

公开(公告)号：US5544277A

公开(公告)日：1996-08-06

申请号：US98682

申请日：1993-07-28

申请人： Raimo Bakis ,  Ponani S. Gopalakrishnan ,  Dimitri Kanevsky ,  Arthur J. Nadas ,  David Nahamoo ,  Michael A. Picheny ,  Jan Sedivy

发明人： Raimo Bakis ,  Ponani S. Gopalakrishnan ,  Dimitri Kanevsky ,  Arthur J. Nadas ,  David Nahamoo ,  Michael A. Picheny ,  Jan Sedivy

IPC分类号： G06F3/16 ,  G10L11/00 ,  G10L15/02 ,  G10L15/10 ,  G10L15/20 ,  H03M7/30 ,  G10L9/00

CPC分类号： G10L15/02 ,  G10L15/20

摘要： A speech coding apparatus and method measures the values of at least first and second different features of an utterance during each of a series of successive time intervals. For each time interval, a feature vector signal has a first component value equal to a first weighted combination of the values of only one feature of the utterance for at least two time intervals. The feature vector signal has a second component value equal to a second weighted combination, different from the first weighted combination, of the values of only one feature of the utterance for at least two time intervals. The resulting feature vector signals for a series of successive time intervals form a coded representation of the utterance. In one embodiment, a first weighted mixture signal has a value equal to a first weighted mixture of the values of the features of the utterance during a single time interval. A second weighted mixture signal has a value equal to a second weighted mixture, different from the first weighted mixture, of the values of the features of the utterance during a single time interval. The first component value of each feature vector signal is equal to a first weighted combination of the values of only the first weighted mixture signals for at least two time intervals, and the second component value of each feature vector signal is equal to a second weighted combination, different from the first weighted combination, of the values of only the second weighted mixture for at least two time intervals.

摘要翻译： 语音编码装置和方法在一系列连续时间间隔的每一个期间测量话音的至少第一和第二不同特征的值。 对于每个时间间隔，特征向量信号具有等于至少两个时间间隔的仅一个特征的值的第一加权组合的第一分量值。 特征向量信号具有等于至少两个时间间隔的话语的一个特征的值的等于第一加权组合的第二加权组合的第二分量值。 所得到的一系列连续时间间隔的特征矢量信号形成话音的编码表示。 在一个实施例中，第一加权混合信号具有等于在单个时间间隔期间话音特征值的第一加权混合的值。 第二加权混合信号具有等于在单个时间间隔期间话音特征的值的与第一加权混合不同的第二加权混合的值。 每个特征向量信号的第一分量值等于至少两个时间间隔的仅第一加权混合信号的值的第一加权组合，并且每个特征向量信号的第二分量值等于第二加权组合 与第一加权组合不同的是仅至少两个时间间隔的第二加权混合值的值。

公开(公告)号：US5280562A

公开(公告)日：1994-01-18

申请号：US770495

申请日：1991-10-03

申请人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Edward A. Epstein ,  John M. Lucassen ,  David Nahamoo ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Edward A. Epstein ,  John M. Lucassen ,  David Nahamoo ,  Michael A. Picheny

IPC分类号： G10L19/00 ,  G10L15/02 ,  G10L19/02 ,  H03M7/30 ,  G10L9/02

CPC分类号： G10L19/038 ,  H03M7/3082

摘要： In speech recognition and speech coding, the values of at least two features of an utterance are measured during a series of time intervals to produce a series of feature vector signals. A plurality of single-dimension prototype vector signals having only one parameter value are stored. At least two single-dimension prototype vector signals having parameter values representing first feature values, and at least two other single-dimension prototype vector signals have parameter values representing second feature values. A plurality of compound-dimension prototype vector signals have unique identification values and comprise one first-dimension and one second-dimension prototype vector signal. At least two compound-dimension prototype vector signals comprise the same first-dimension prototype vector signal. The feature values of each feature vector signal are compared to the parameter values of the compound-dimension prototype vector signals to obtain prototype match scores. The identification values of the compound-dimension prototype vector signals having the best prototype match scores for the feature vectors signals are output as a sequence of coded representations of an utterance to be recognized. A match score, comprising an estimate of the closeness of a match between a speech unit and the sequence of coded representations of the utterance, is generated for each of a plurality of speech units. At least one speech subunit, of one or more best candidate speech units having the best match scores, is displayed.

摘要翻译： 在语音识别和语音编码中，在一系列时间间隔期间测量话音的至少两个特征的值，以产生一系列特征向量信号。 存储仅具有一个参数值的多个单维原型矢量信号。 具有表示第一特征值的参数值和至少两个其它单维原型矢量信号的至少两个单维原型矢量信号具有表示第二特征值的参数值。 多个复合尺寸原型矢量信号具有唯一的识别值，并且包括一个第一维和一个第二维原型矢量信号。 至少两个复合维度原型矢量信号包括相同的第一维原型矢量信号。 将每个特征向量信号的特征值与化合物维度原型矢量信号的参数值进行比较，以获得原型匹配分数。 具有特征矢量信号的具有最佳原型匹配分数的复合维度原型矢量信号的识别值被输出为将被识别的话语的编码表示的序列。 针对多个语音单元中的每一个生成包括语音单元与语音编码表示序列之间的匹配的接近度的估计的匹配分数。 显示具有最佳匹配分数的一个或多个最佳候选语音单元的至少一个语音子单元。

公开(公告)号：US5276766A

公开(公告)日：1994-01-04

申请号：US730714

申请日：1991-07-16

申请人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. DeSouza ,  David Nahamoo ,  Michael A. Picheny

发明人： Lalit R. Bahl ,  Jerome R. Bellegarda ,  Peter V. DeSouza ,  David Nahamoo ,  Michael A. Picheny

IPC分类号： G10L19/00 ,  G10L15/02 ,  G10L15/06 ,  G10L9/04

CPC分类号： G10L15/063

摘要： An apparatus for generating a set of acoustic prototype signals for encoding speech includes a memory for storing a training script model comprising a series of word-segment models. Each word-segment model comprises a series of elementary models. An acoustic measure is 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. An acoustic matcher is 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 a cluster processor 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 a memory 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.

摘要翻译： 一种用于生成用于编码语音的声原型信号的集合的装置包括用于存储包括一系列字段模型的训练脚本模型的存储器。 每个单词段模型包括一系列基本模型。 提供了一种声学测量，用于在一系列时间间隔的每一个期间测量训练脚本的发音的至少一个特征的值，以产生表示发音的特征值的一系列特征向量信号。 提供声学匹配器用于估计通过训练脚本模型的至少一个路径，其将产生整个测量的特征向量信号的一系列。 从估计的路径，估计将产生每个特征向量信号的训练脚本模型中的基本模型。 该装置还包括用于将特征向量信号聚类成多个聚类的聚类处理器。 群集中的每个特征向量信号对应于单个单词段模型中单个位置中的单个基本模型。 每个聚类信号具有等于信号中所有特征向量的特征值的平均值的聚类值。 最后，该装置包括用于存储多个原型矢量信号的存储器。 每个原型矢量信号对应于基本模型，具有标识符，并且包括至少两个分区值。 分区值等于对应于基本模型的一个或多个聚类信号的聚类值的组合。

公开(公告)号：US5222146A

公开(公告)日：1993-06-22

申请号：US781440

申请日：1991-10-23

申请人： Latit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

发明人： Latit R. Bahl ,  Peter V. De Souza ,  Ponani S. Gopalakrishnan ,  Michael A. Picheny

IPC分类号： G10L15/02 ,  G10L15/14 ,  G10L19/00

CPC分类号： G10L15/02 ,  G10L19/0018

摘要： A speech coding and speech recognition apparatus. The value of at least one feature of an utterance is measured over each of a series of successive time intervals to produce a series of feature vector signals. The closeness of the feature value of each feature vector signal to the parameter value of each of a set of prototype vector signals is determined to obtain prototype match scores for each vector signal and each prototype vector signal. For each feature vector signal, first-rank and second-rank scores are associated with the prototype vector signals having the best and second best prototype match scores, respectively. For each feature vector signal, at least the identification value and the rank score of the first-ranked and second-ranked prototype vector signals are output as a coded utterance representation signal of the feature vector signal, to produce a series of coded utterance representation signals. For each of a plurality of speech units, a probabilistic model has a plurality of model outputs, and output probabilities for each model output. Each model output comprises the identification value of a prototype vector and a rank score. For each speech unit, a match score comprises an estimate of the probability that the probabilistic model of the speech unit would output a series of model outputs matching a reference series comprising the identification value and rank score of at least one prototype vector from each coded utterance representation signal in the series of coded utterance representation signals.