公开(公告)号：US5956677A

公开(公告)日：1999-09-21

申请号：US644273

申请日：1996-05-10

Applicant: Ken-ichi Iso

Inventor： Ken-ichi Iso

IPC: G10L15/06 ,  G10L15/10 ,  G10L15/14 ,  G10L15/18 ,  G10L5/06

CPC classification number: G10L15/187 ,  G10L15/06 ,  G10L15/10 ,  G10L15/142 ,  G10L15/1815

Abstract: A speech recognizer includes a feature extracting unit for analyzing an input speech to extract a feature vector of the input speech. A speech data memory stores speech data and symbol trains of the input speech. A reference pattern memory stores sets each of a given partial symbol train of a word presented for recognition and an index of speech data with the expression thereof containing the partial symbol train in the speech data memory. A distance calculating unit reads out speech data corresponding to a partial symbol train stored in the reference pattern memory from the speech data memory, and calculates a distance between the partial symbol train read out from the reference pattern memory and a particular section of the input speech. A pattern matching unit derives, with respect to each word presented for recognition, a division of the subject word interval which minimizes the sum of distances of the input speech sections over an entire word interval. A recognition result calculating unit outputs, as a recognition result, a word presented for recognition, which gives the minimum one of the distances between the input speech data output of the pattern matching unit and all the words presented for recognition.

Abstract translation: 语音识别器包括特征提取单元，用于分析输入语音以提取输入语音的特征向量。 语音数据存储器存储输入语音的语音数据和符号列。 参考图形存储器存储用于识别的单词的给定部分符号串和语音数据的索引，其中包含语音数据存储器中的部分符号列表的表达式。 距离计算单元从语音数据存储器读出对应于存储在参考图形存储器中的部分符号列的语音数据，并且计算从参考图形存储器读出的部分符号列与输入语音的特定部分之间的距离 。 模式匹配单元相对于用于识别的每个单词导出将整个单词间隔上的输入语音段的距离之和最小化的主题间隔的划分。 识别结果计算单元作为识别结果输出呈现用于识别的单词，其给出了模式匹配单元的输入语音数据输出与所呈现的用于识别的所有单词之间的距离中的最小距离。

公开(公告)号：US5600753A

公开(公告)日：1997-02-04

申请号：US270416

申请日：1994-07-05

Applicant: Ken-ichi Iso

Inventor： Ken-ichi Iso

IPC: G06F15/18 ,  G06N3/00 ,  G06N3/04 ,  G06N99/00 ,  G10L15/02 ,  G10L15/08 ,  G10L15/14 ,  G10L15/16 ,  G01L1/06 ,  G06F15/00

CPC classification number: G06K9/6297 ,  G06N3/049 ,  G10L15/16

Abstract: A speech recognition method according to the present invention uses distances calculated through a variance weighting process using covariance matrixes as the local distances (prediction residuals) between the feature vectors of input syllables/sound elements and predicted vectors formed by different statuses of reference neural prediction models (NPM's) using finite status transition networks. The category to minimize the accumulated value of these local distances along the status transitions of all the prediction models is figured out by dynamic programming, and used as the recognition output. Learning of the reference prediction models used in this recognition method is accomplished by repeating said distance calculating process and the process to correct the parameters of the different statuses and the covariance matrixes of said prediction models in the direction of reducing the distance between the learning patterns whose category is known and the prediction models of the same category as this known category, and what have satisfied prescribed conditions of convergence through these calculating and correcting processes are determined as reference pattern models.

Abstract translation: 根据本发明的语音识别方法使用通过使用协方差矩阵的方差加权处理计算的距离作为输入音节/声音元素的特征向量与由参考神经预测模型的不同状态形成的预测向量之间的局部距离（预测残差） （NPM）使用有限状态转换网络。 根据所有预测模型的状态转换，将这些局部距离累积值最小化的类别通过动态规划来计算，并用作识别输出。 在该识别方法中使用的参考预测模型的学习是通过重复所述距离计算处理和校正所述预测模型的不同状态的参数和所述预测模型的协方差矩阵的方法来实现的，所述方法减少了学习模式之间的距离 类别是已知的，并且与该已知类别相同类别的预测模型以及通过这些计算和校正过程满足规定的收敛条件被确定为参考模型模型。

公开(公告)号：US06275799B1

公开(公告)日：2001-08-14

申请号：US08384457

申请日：1995-02-02

Applicant: Ken-ichi Iso

Inventor： Ken-ichi Iso

IPC: G10L1502

CPC classification number: G10L15/063

Abstract: A first parameter set constituting reference patterns of each category in speech recognition based on pattern matching with a reference pattern is to be determined from a plurality of learning utterance data. The first parameter set is determined so that a third evaluation function, represented by a sum of a first evaluation function and a second evaluation function is maximized. The first evaluation function represents a matching degree between all learning utterances and corresponding reference patterns. The second evaluation function represents a matching degree between elements of the first parameter set.

Abstract translation: 基于与参考图案的模式匹配，构成语音识别中的每个类别的参考图案的第一参数集合将从多个学习话语数据确定。 确定第一参数集，使得由第一评估函数和第二评估函数的和表示的第三评估函数被最大化。 第一评估函数表示所有学习话语和对应参考模式之间的匹配度。 第二评估函数表示第一参数集的元素之间的匹配度。