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公开(公告)号：US09679556B2
公开(公告)日：2017-06-13
申请号：US13974123
申请日：2013-08-23
CPC分类号： G10L15/063
摘要： A system and method are presented for selectively biased linear discriminant analysis in automatic speech recognition systems. Linear Discriminant Analysis (LDA) may be used to improve the discrimination between the hidden Markov model (HMM) tied-states in the acoustic feature space. The between-class and within-class covariance matrices may be biased based on the observed recognition errors of the tied-states, such as shared HMM states of the context dependent tri-phone acoustic model. The recognition errors may be obtained from a trained maximum-likelihood acoustic model utilizing the tied-states which may then be used as classes in the analysis.
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2.发明申请METHOD AND SYSTEM FOR ACOUSTIC DATA SELECTION FOR TRAINING THE PARAMETERS OF AN ACOUSTIC MODEL 有权用于训练声学模型参数的声学数据选择的方法和系统公开(公告)号：US20140046662A1
公开(公告)日：2014-02-13
申请号：US13959171
申请日：2013-08-05
IPC分类号： G10L15/06
CPC分类号： G10L15/063 , G10L15/144 , G10L2015/025
摘要： A system and method are presented for acoustic data selection of a particular quality for training the parameters of an acoustic model, such as a Hidden Markov Model and Gaussian Mixture Model, for example, in automatic speech recognition systems in the speech analytics field. A raw acoustic model may be trained using a given speech corpus and maximum likelihood criteria. A series of operations are performed, such as a forced Viterbi-alignment, calculations of likelihood scores, and phoneme recognition, for example, to form a subset corpus of training data. During the process, audio files of a quality that does not meet a criterion, such as poor quality audio files, may be automatically rejected from the corpus. The subset may then be used to train a new acoustic model.
摘要翻译： 提出了一种系统和方法，用于例如在语音分析领域的自动语音识别系统中用于训练诸如隐马尔可夫模型和高斯混合模型的声学模型的参数的特定质量的声学数据选择。 可以使用给定语音语料库和最大似然准则来训练原始声学模型。 执行一系列操作，例如强制维特比对齐，可能性分数的计算和音素识别，例如形成训练数据的子集语料库。 在此过程中，可能会自动从语料库中拒绝不符合标准（例如质量差的音频文件）的质量的音频文件。 然后该子集可用于训练新的声学模型。
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3.发明申请Method and System for Selectively Biased Linear Discriminant Analysis in Automatic Speech Recognition Systems 有权自动语音识别系统中选择性偏置线性判别分析方法与系统公开(公告)号：US20140058731A1
公开(公告)日：2014-02-27
申请号：US13974123
申请日：2013-08-23
IPC分类号： G10L15/06
CPC分类号： G10L15/063
摘要： A system and method are presented for selectively biased linear discriminant analysis in automatic speech recognition systems. Linear Discriminant Analysis (LDA) may be used to improve the discrimination between the hidden Markov model (HMM) tied-states in the acoustic feature space. The between-class and within-class covariance matrices may be biased based on the observed recognition errors of the tied-states, such as shared HMM states of the context dependent tri-phone acoustic model. The recognition errors may be obtained from a trained maximum-likelihood acoustic model utilizing the tied-states which may then be used as classes in the analysis.
摘要翻译： 提出了一种系统和方法，用于自动语音识别系统中的选择性偏置线性判别分析。 线性判别分析（LDA）可用于改善声学特征空间中隐马尔可夫模型（HMM）绑定状态之间的区别。 基于观察到的绑定状态的识别误差，例如上下文相关三电话声学模型的共享HMM状态，类间和协方差矩阵之间可能是偏置的。 识别误差可以从训练有素的最大似然声学模型中获得，该模型可以用作分析中的类别。
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4.发明授权公开(公告)号：US09972306B2
公开(公告)日：2018-05-15
申请号：US13959171
申请日：2013-08-05
CPC分类号： G10L15/063 , G10L15/144 , G10L2015/025
摘要： A system and method are presented for acoustic data selection of a particular quality for training the parameters of an acoustic model, such as a Hidden Markov Model and Gaussian Mixture Model, for example, in automatic speech recognition systems in the speech analytics field. A raw acoustic model may be trained using a given speech corpus and maximum likelihood criteria. A series of operations are performed, such as a forced Viterbi-alignment, calculations of likelihood scores, and phoneme recognition, for example, to form a subset corpus of training data. During the process, audio files of a quality that does not meet a criterion, such as poor quality audio files, may be automatically rejected from the corpus. The subset may then be used to train a new acoustic model.
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