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    • 51. 发明授权
    • Robustness to environmental changes of a context dependent speech recognizer
    • 对上下文相关语音识别器的环境变化的鲁棒性
    • US08719023B2
    • 2014-05-06
    • US12785375
    • 2010-05-21
    • Xavier Menendez-PidalRuxin Chen
    • Xavier Menendez-PidalRuxin Chen
    • G10L15/14
    • G10L15/144G10L15/187G10L2015/022G10L2015/0631
    • An apparatus to improve robustness to environmental changes of a context dependent speech recognizer for an application, that includes a training database to store sounds for speech recognition training, a dictionary to store words supported by the speech recognizer, and a speech recognizer training module to train a set of one or more multiple state Hidden Markov Models (HMMs) with use of the training database and the dictionary. The speech recognizer training module performs a non-uniform state clustering process on each of the states of each HMM, which includes using a different non-uniform cluster threshold for at least some of the states of each HMM to more heavily cluster and correspondingly reduce a number of observation distributions for those of the states of each HMM that are less empirically affected by one or more contextual dependencies.
    • 一种用于提高对应用的上下文相关语音识别器对环境变化的鲁棒性的装置,其包括用于存储用于语音识别训练的声音的训练数据库,用于存储由语音识别器支持的单词的词典和用于训练的语音识别器训练模块 使用训练数据库和字典的一组或多个多状态隐马尔可夫模型(HMM)。 语音识别器训练模块对每个HMM的每个状态执行不均匀的状态聚类处理,其包括对于每个HMM的至少一些状态使用不同的非均匀簇阈值来进行更大的聚类,并相应地减少 每个HMM的状态的观察分布的数量较少受一个或多个上下文相关性的经验影响。
    • 52. 发明授权
    • Method and system for modeling a common-language speech recognition, by a computer, under the influence of a plurality of dialects
    • 在多个方言的影响下,由计算机对共同语言语音识别进行建模的方法和系统
    • US08712773B2
    • 2014-04-29
    • US12608191
    • 2009-10-29
    • Fang ZhengXi XiaoLinquan LiuZhan YouWenxiao CaoMakoto AkabaneRuxin ChenYoshikazu Takahashi
    • Fang ZhengXi XiaoLinquan LiuZhan YouWenxiao CaoMakoto AkabaneRuxin ChenYoshikazu Takahashi
    • G10L15/00
    • G10L15/187
    • The present invention relates to a method for modeling a common-language speech recognition, by a computer, under the influence of multiple dialects and concerns a technical field of speech recognition by a computer. In this method, a triphone standard common-language model is first generated based on training data of standard common language, and first and second monophone dialectal-accented common-language models are based on development data of dialectal-accented common languages of first kind and second kind, respectively. Then a temporary merged model is obtained in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to a first confusion matrix obtained by recognizing the development data of first dialectal-accented common language using the standard common-language model. Finally, a recognition model is obtained in a manner that the second dialectal-accented common-language model is merged into the temporary merged model according to a second confusion matrix generated by recognizing the development data of second dialectal-accented common language by the temporary merged model. This method effectively enhances the operating efficiency and admittedly raises the recognition rate for the dialectal-accented common language. The recognition rate for the standard common language is also raised.
    • 本发明涉及一种通过计算机在多种方言的影响下对共同语言语音识别进行建模的方法,并涉及计算机的语音识别技术领域。 在这种方法中,首先基于标准通用语言的训练数据生成三通电话标准通用语言模型,第一和第二单音方言重点共同语言模型基于第一种方言重音通用语言的开发数据, 第二类。 然后按照第一个方言重音的共同语言模型按照第一个混淆矩阵将第一个方言重音的共同语言模型合并到第一个混淆矩阵中的方式获得临时合并模型,该第一混淆矩阵通过使用 标准通用语言模型。 最后,根据通过临时合并识别第二方言重音的共同语言的开发数据产生的第二混淆矩阵,将第二方言重音的共同语言模型合并到临时合并模型中,获得识别模型 模型。 这种方法有效提高了运营效率,并且提高了方言重音普通语言的识别率。 标准通用语言的识别率也提高了。
    • 56. 发明申请
    • Structure for Grammar and Dictionary Representation in Voice Recognition and Method for Simplifying Link and Node-Generated Grammars
    • 语音识别中的语法和词典表示结构以及简化链接和节点生成语法的方法
    • US20110191107A1
    • 2011-08-04
    • US13031104
    • 2011-02-18
    • Gustavo Hernandez AbregoRuxin Chen
    • Gustavo Hernandez AbregoRuxin Chen
    • G10L15/18
    • G10L15/193G10L15/285
    • A speech recognition engine is provided with an acoustic model and a layered grammar and dictionary library. The layered grammar and dictionary library includes a language and non-grammar layer that supplies types of rules a grammar definition layer can use and defines non-grammar the speech recognition engine should ignore. The layered grammar and dictionary library also includes a dictionary layer that defines phonetic transcriptions for word groups the speech recognition engine is meant to recognize when voice input is received. The layered grammar and dictionary library further includes a grammar definition layer that applies rules from the language and non-grammar layer to define combinations of word groups the speech recognition system is meant to recognize. Voice input is received at a speech recognition engine and is processed using the acoustic model and the layered grammar and dictionary library.
    • 语音识别引擎设有声学模型和分层语法和字典库。 分层语法和字典库包括语言和非语法层,提供语法定义层可以使用的规则类型,并定义语音识别引擎应忽略的非语法。 分层语法和字典库还包括字典层,其定义语音识别引擎在接收到语音输入时识别的单词组的语音转录。 分层语法和字典库还包括语法定义层,其应用语言和非语法层的规则来定义语音识别系统意图识别的单词组的组合。 在语音识别引擎处接收语音输入,并使用声学模型和分层语法和字典库进行处理。
    • 60. 发明申请
    • Method and system for Gaussian probability data bit reduction and computation
    • 高斯概率数据位减少和计算的方法和系统
    • US20070112566A1
    • 2007-05-17
    • US11273223
    • 2005-11-12
    • Ruxin Chen
    • Ruxin Chen
    • G10L15/00
    • G10L15/285G10L15/14
    • Use of runtime memory may be reduced in a data processing algorithm that uses one or more probability distribution functions. Each probability distribution function may be characterized by one or more uncompressed mean values and one or more variance values. The uncompressed mean and variance values may be represented by α-bit floating point numbers, where α is an integer greater than 1. The probability distribution functions are converted to compressed probability functions having compressed mean and/or variance values represented as β-bit integers, where β is less than α, whereby the compressed mean and/or variance values occupy less memory space than the uncompressed mean and/or variance values. Portions of the data processing algorithm can be performed with the compressed mean and variance values.
    • 在使用一个或多个概率分布函数的数据处理算法中,运行时存储器的使用可能会减少。 每个概率分布函数可以由一个或多个未压缩平均值和一个或多个方差值来表征。 未压缩的平均值和方差值可以由α位浮点数表示,其中α是大于1的整数。概率分布函数被转换成压缩的概率函数,其具有表示为β位整数的压缩平均值和/或方差值 ,其中beta小于α,由此压缩的平均值和/或方差值比未压缩平均值和/或方差值占据更少的存储空间。 可以使用压缩的平均值和方差值来执行数据处理算法的一部分。