公开(公告)号：US5828999A

公开(公告)日：1998-10-27

申请号：US643521

申请日：1996-05-06

申请人： Jerome R. Bellegarda ,  Yen-Lu Chow

发明人： Jerome R. Bellegarda ,  Yen-Lu Chow

IPC分类号： G06K9/72 ,  G10L15/18 ,  G10L5/06

CPC分类号： G10L15/1815 ,  G06K9/726 ,  G06K2209/01

摘要： A system and method for deriving a large-span semantic language model for a large vocabulary recognition system is disclosed. The method and system maps words from a vocabulary into a vector space, where each word is represented by a vector. After the vectors are mapped to the space, the vectors are clustered into a set of clusters, where each cluster represents a semantic event. After clustering the vectors, a probability that a first word will occur given a history of prior words is computed by (i) calculating a probability that the vector representing the first word belongs to each of the clusters; (ii) calculating a probability of each cluster occurring in a history of prior words; and weighting (i) by (ii) to provide the probability.

摘要翻译： 公开了一种用于导出大型词汇识别系统的大范围语义语言模型的系统和方法。 该方法和系统将词汇从词汇映射到向量空间，其中每个单词由向量表示。 在将向量映射到空间之后，矢量被聚集成一组集群，其中每个集群表示一个语义事件。 在对矢量进行聚类之后，通过（i）计算表示第一个词的向量属于每个簇的概率来计算给定先验词的历史的第一个单词的概率; （ii）计算每个簇在先前单词的历史中发生的概率; 并通过（ii）加权（i）以提供概率。

公开(公告)号：US5757964A

公开(公告)日：1998-05-26

申请号：US901989

申请日：1997-07-29

申请人： Kai-Fu Lee ,  Yen-Lu Chow ,  Kamil Grajski

发明人： Kai-Fu Lee ,  Yen-Lu Chow ,  Kamil Grajski

IPC分类号： G06K9/62 ,  G06K9/72

CPC分类号： G06K9/6297 ,  G06K9/6255

摘要： A system for automatic subcharacter unit and lexicon generation for handwriting recognition comprises a processing unit, a handwriting input device, and a memory wherein a segmentation unit, a subcharacter generation unit, a lexicon unit, and a modeling unit reside. The segmentation unit generates feature vectors corresponding to sample characters. The subcharacter generation unit clusters feature vectors and assigns each feature vector associated with a given cluster an identical label. The lexicon unit constructs a lexical graph for each character in a character set. The modeling unit generates a Hidden Markov Model for each set of identically-labeled feature vectors. After a first set of lexical graphs and Hidden Markov Models have been created, the subcharacter generation unit determines for each feature vector which Hidden Markov Model produces a highest likelihood value. The subcharacter generation unit relabels each feature vector according to the highest likelihood value, after which the lexicon unit and the modeling unit generate a new set of lexical graphs and a new set of Hidden Markov models, respectively. The feature vector relabeling, lexicon generation, and Hidden Markov Model generation are performed iteratively until a convergence criterion is met. The final set of Hidden Markov Model model parameters provide a set of subcharacter units for handwriting recognition, where the subcharacter units are derived from information inherent in the sample characters themselves.

摘要翻译： 用于手写识别的自动子字符单元和词典生成的系统包括处理单元，手写输入装置和存储器，其中存在分割单元，子字符生成单元，词典单元和建模单元。 分割单元生成与采样字符对应的特征矢量。 子字符生成单元簇特征向量并且将与给定簇相关联的每个特征向量分配给相同的标签。 词典单元为字符集中的每个字符构成一个词汇图。 建模单元为每组相同标记的特征向量生成隐马尔科夫模型。 在创建了第一组词汇图和隐马尔科夫模型之后，子字符生成单元为每个特征向量确定隐马尔可夫模型产生最高似然值。 子字符生成单元根据最高似然值重新标记每个特征向量，之后词法单元和建模单元分别生成一组新的词法图和一组新的隐马尔可夫模型。 迭代地执行特征向量重新标记，词法生成和隐马尔科夫模型生成，直到满足收敛标准。 最后一组隐马尔可夫模型参数提供了一组用于手写识别的子字符单元，其中子字符单元是从样本字符本身固有的信息导出的。

公开(公告)号：US5706397A

公开(公告)日：1998-01-06

申请号：US539346

申请日：1995-10-05

申请人： Yen-Lu Chow

发明人： Yen-Lu Chow

IPC分类号： G10L15/08 ,  G10L5/06 ,  G10L9/00

CPC分类号： G10L15/08

摘要： A method of constructing a new active list of phone models from an existing active list of phone models during acoustic matching of a speech recognition system is described. A vector quantized speech vector is compared against each of the phone models in the existing active list to obtain a phone best score for each of the phone models of the existing active list. A best phone best score is determined among all the phone best scores of the phone models to obtain a global best score. A phone model of the phone models from the existing active list is added to the new active list of phone models if the phone best score of that phone model is within a first predetermined value of the global best score. A next phone model of the existing phone of the existing active list is added to the new active list if the phone ending score of that existing phone is within a second predetermined value of a best score of the existing phone model. A next (e.g. first) phone model of a next word of a particular phone model of the existing active list is added to the new active list if the ending score of that particular phone model is within a third predetermined value of the global best score.

摘要翻译： 描述了在语音识别系统的声匹配期间从电话模型的现有活动列表构建电话模型的新的活动列表的方法。 将矢量量化语音向量与现有活动列表中的每个电话模型进行比较，以获得现有活动列表的每个电话模型的手机最佳分数。 所有最好的手机最好的成绩是确定在所有手机最好的得分的手机模型获得全球最佳分数。 如果该手机型号的手机最佳分数在全球最佳分数的第一预定值内，则将来自现有活动列表的手机型号的手机型号添加到手机型号的新活动列表中。 如果现有电话的电话结束分数在现有电话模型的最佳分数的第二预定值内，则将现有活动列表的现有电话的下一个电话模型添加到新的活动列表。 如果该特定电话模型的结束分数在全球最佳分数的第三预定值内，则将现有活动列表的特定电话模型的下一个单词的下一个（例如，第一个）电话模型添加到新的活动列表中。

公开(公告)号：US5535305A

公开(公告)日：1996-07-09

申请号：US999293

申请日：1992-12-31

申请人： Alejandro Acero ,  Yen-Lu Chow ,  Kai-Fu Lee

发明人： Alejandro Acero ,  Yen-Lu Chow ,  Kai-Fu Lee

IPC分类号： G06K9/62 ,  G10L15/02 ,  G10L15/14 ,  G10L5/06

CPC分类号： G10L15/144 ,  G06K9/6217

摘要： A speech recognition memory compression method and apparatus subpartitions probability density function (pdf) space along the hidden Markov model (HMM) index into packets of typically 4 to 8 log-pdf values. Vector quantization techniques are applied using a logarithmic distance metric and a probability weighted logarithmic probability space for the splitting of clusters. Experimental results indicate a significant reduction in memory can be obtained with little increase in overall speech recognition error.

摘要翻译： 一种将隐马尔可夫模型（HMM）索引的语音识别存储器压缩方法和装置子分类概率密度函数（pdf）空间通常为4到8个log-pdf值的分组。 使用对数距离度量和用于分割簇的概率加权对数概率空间来应用矢量量化技术。 实验结果表明，在语音识别总误差增加很小的情况下可以显着降低记忆。