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1.发明授权Speech coding apparatus having speaker dependent prototypes generated from nonuser reference data 失效具有由非用户参考数据生成的具有说话者依赖原型的语音编码装置
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
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。
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2.发明授权Speech coding apparatus and method for generating acoustic feature vector component values by combining values of the same features for multiple time intervals 失效用于通过组合多个时间间隔的相同特征的值来生成声学特征矢量分量值的语音编码装置和方法
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
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。
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3.发明授权Method and apparatus for finding the best splits in a decision tree for a language model for a speech recognizer 失效在用于语音识别器的语言模型的决策树中找到最佳分割的方法和装置
公开(公告)号：US5263117A
公开(公告)日：1993-11-16
申请号：US427420
申请日：1989-10-26
申请人： Arthur J. Nadas , David Nahamoo
发明人： Arthur J. Nadas , David Nahamoo
CPC分类号： G10L15/197
摘要： A method and apparatus for finding the best or near best binary classification of a set of observed events, according to a predictor feature X so as to minimize the uncertainty in the value of a category feature Y. Each feature has three or more possible values. First, the predictor feature value and the category feature value of each event is measured. The events are then split, arbitrarily, into two sets of predictor feature values. From the two sets of predictor feature values, an optimum pair of sets of category feature values is found having the lowest uncertainty in the value of the predictor feature. From the two optimum sets of category feature values, an optimum pair of sets is found having the lowest uncertainty in the value of the category feature. An event is then classified according to whether its predictor feature value is a member of a set of optimal predictor feature values.
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公开(公告)号：US4926488A
公开(公告)日：1990-05-15
申请号：US71687
申请日：1987-07-09
申请人： Arthur J. Nadas , David Nahamoo
发明人： Arthur J. Nadas , David Nahamoo
摘要： In a speech processor system in which prototype vectors of speech are generated by an acoustic processor under reference noise and known ambient conditions and in which feature vectors of speech are generated during varying noise and other ambient and recording conditions, normalized vectors are generated to reflect the form the feature vectors would have if generated under the reference conditions. The normalized vectors are generated by: (a) applying an operator function A.sub.i to a set of feature vectors x occurring at or before time interval i to yield a normalized vector y.sub.i =A.sub.i (x); (b) determining a distance error vector E.sub.i by which the normalized vector is projectively moved toward the closest prototype vector to the normalized vector y.sub.i ; (c) up-dating the operator function for next time interval to correspond to the most recently determined distance error vector; and (d) incrementing i to the next time interval and repeating steps (a) through (d) wherein the feature vector corresponding to the incremented i value has the most recent up-dated operator function applied thereto. With successive time intervals, successive normalized vectors are generated based on a successively up-dated operator function. For each normalized vector, the closest prototype thereto is associated therewith. The string of normalized vectors or the string of associated prototypes (or respective label identifiers thereof) or both provide output from the acoustic processor.
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