-
1.发明授权Apparatus and method of grouping utterances of a phoneme into context-dependent categories based on sound-similarity for automatic speech recognition 失效基于自动语音识别的声音相似性将音素的语音分组成上下文相关类别的装置和方法
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
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。
-
2.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。
-
3.发明授权
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
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.
-
4.发明授权Speech recognizer having a speech coder for an acoustic match based on context-dependent speech-transition acoustic models 失效语音识别器具有基于上下文相关语音 - 过渡声学模型的用于声学匹配的语音编码器
公开(公告)号：US5333236A
公开(公告)日：1994-07-26
申请号：US942862
申请日：1992-09-10
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。
-
5.发明授权
公开(公告)号：US5233681A
公开(公告)日：1993-08-03
申请号：US874271
申请日：1992-04-24
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.
-
6.发明授权Speech recognition apparatus having a speech coder outputting acoustic prototype ranks 失效具有语音编码器的语音识别装置输出声学原型排序
公开(公告)号：US5222146A
公开(公告)日：1993-06-22
申请号：US781440
申请日：1991-10-23
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.
-
7.发明授权Speech coding apparatus having acoustic prototype vectors generated by tying to elementary models and clustering around reference vectors 失效语音编码装置具有通过绑定到基本模型并围绕参考矢量聚类而生成的声学原型矢量
公开(公告)号：US5497447A
公开(公告)日：1996-03-05
申请号：US28028
申请日：1993-03-08
CPC分类号： G10L15/063
摘要： A speech coding apparatus in which measured acoustic feature vectors are each represented by the best matched prototype vector. The prototype vectors are generated by storing a model of a training script comprising a series of elementary models. The value of at least one feature of a training utterance of the training script is measured over each of a series of successive time intervals to produce a series of training feature vectors. A first set of training feature vectors corresponding to a first elementary model in the training script is identified. The feature value of each training feature vector signal in the first set is compared to the parameter value of a first reference vector signal to obtain a first closeness score, and is compared to the parameter value of a second reference vector to obtain a second closeness score for each training feature vector. For each training feature vector in the first set, the first closeness score is compared with the second closeness score to obtain a reference match score. A first subset contains those training feature vectors in the first set having reference match scores better than a threshold Q, and a second subset contains those having reference match scores less than the threshold Q. One or more partition values are generated for a first prototype vector frown the first subset of training feature vectors, and one or more additional partition values are generated for the first prototype vector from the second subset of training feature vectors.
摘要翻译： 一种语音编码装置，其中测量的声学特征矢量各自由最佳匹配的原型矢量表示。 通过存储包括一系列基本模型的训练脚本的模型来生成原型向量。 在一系列连续时间间隔中的每一个上测量训练脚本的训练话语的至少一个特征的值，以产生一系列训练特征向量。 识别与训练脚本中的第一个基本模型对应的第一组训练特征向量。 将第一组中的每个训练特征向量信号的特征值与第一参考矢量信号的参数值进行比较以获得第一接近度分数，并将其与第二参考矢量的参数值进行比较以获得第二接近度分数 对于每个训练特征向量。 对于第一组中的每个训练特征向量，将第一接近度得分与第二接近度得分进行比较以获得参考匹配得分。 第一子集包含具有比阈值Q更好的参考匹配分数的第一集合中的那些训练特征向量，并且第二子集包含具有小于阈值Q的参考匹配分数的训练特征向量。对于第一原型矢量生成一个或多个分区值 使训练特征向量的第一子集皱眉，并且从训练特征向量的第二子集为第一原型向量生成一个或多个附加分区值。
-
8.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。
-
公开(公告)号：US5522011A
公开(公告)日：1996-05-28
申请号：US127392
申请日：1993-09-27
CPC分类号： G10L19/038
摘要： A speech coding apparatus and method uses classification rules to code an utterance while consuming fewer computing resources. 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. The classification rules comprise at least first and second sets of classification rules. The first set of classification rules map each feature vector signal from a set of all possible feature vector signals to exactly one of at least two disjoint subsets of feature vector signals. The second set of classification rules map each feature vector signal in a subset of feature vector signals to exactly one of at least two different classes of prototype vector signals. Each class contains a plurality of prototype vector signals. According to the classification rules, a first feature vector signal is mapped to a first class of prototype vector signals. The closeness of the feature value of the first feature vector signal is compared to the parameter values of only the prototype vector signals in the first class of prototype vector signals to obtain prototype match scores for the first feature vector signal and each prototype vector signal in the first class. At least the identification value of at least the prototype vector signal having the best prototype match score is output as a coded utterance representation signal of the first feature vector signal.
摘要翻译： 语音编码装置和方法使用分类规则来编码话语，同时消耗更少的计算资源。 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。 分类规则至少包括第一组和第二组分类规则。 第一组分类规则将来自一组所有可能特征向量信号的每个特征向量信号映射到特征向量信号的至少两个不相交子集中的一个。 第二组分类规则将特征向量信号的子集中的每个特征向量信号精确地映射到至少两个不同类型的原型矢量信号中的一个。 每个类都包含多个原型矢量信号。 根据分类规则，将第一特征向量信号映射到第一类原型矢量信号。 将第一特征向量信号的特征值的接近度与仅第一类原型矢量信号中的原型矢量信号的参数值进行比较，以获得第一特征向量信号的原型匹配分数和 一等课 至少具有最佳原型匹配分数的原型矢量信号的识别值被输出为第一特征向量信号的编码话音表示信号。
-
10.发明授权Method and apparatus for estimating phone class probabilities a-posteriori using a decision tree 失效用于使用决策树估计电话类概率的方法和装置
公开(公告)号：US5680509A
公开(公告)日：1997-10-21
申请号：US312584
申请日：1994-09-27
CPC分类号： G10L15/063 , G10L15/08
摘要： A method and apparatus for estimating the probability of phones, a-posteriori, in the context of not only the acoustic feature at that time, but also the acoustic features in the vicinity of the current time, and its use in cutting down the search-space in a speech recognition system. The method constructs and uses a decision tree, with the predictors of the decision tree being the vector-quantized acoustic feature vectors at the current time, and in the vicinity of the current time. The process starts with an enumeration of all (predictor, class) events in the training data at the root node, and successively partitions the data at a node according to the most informative split at that node. An iterative algorithm is used to design the binary partitioning. After the construction of the tree is completed, the probability distribution of the predicted class is stored at all of its terminal leaves. The decision tree is used during the decoding process by tracing a path down to one of its leaves, based on the answers to binary questions about the vector-quantized acoustic feature vector at the current time and its vicinity.
摘要翻译： 在不仅在当时的声学特征以及当前时间附近的声学特征的上下文中估计电话的概率的方法和装置，以及其用于减少搜索 - 语音识别系统中的空间。 该方法构造并使用决策树，其中决策树的预测变量是当前时间和当前时间附近的矢量量化的声学特征向量。 该过程从在根节点的训练数据中的所有（预测器，类）事件的枚举开始，并且根据该节点处的最多信息拆分在节点处依次划分数据。 迭代算法用于设计二进制分区。 树完成后，预测类的概率分布存储在其所有终端叶上。 基于对当前时间及其附近的向量量化声学特征向量的二进制问题的答案，在解码过程中使用决策树通过跟踪到其叶子之一的路径。
-
-
-
-
-
-
-
-
-
搜索结果
15 条记录 (耗时 0.026 秒)
