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公开(公告)号：US20160240190A1
公开(公告)日：2016-08-18
申请号：US15042309
申请日：2016-02-12
Inventor： Sung Joo LEE , Byung Ok KANG , Jeon Gue PARK , Yun Keun LEE , Hoon CHUNG
CPC classification number: G10L15/142 , G10L15/063 , G10L15/16 , G10L21/02
Abstract: Provided is an apparatus for large vocabulary continuous speech recognition (LVCSR) based on a context-dependent deep neural network hidden Markov model (CD-DNN-HMM) algorithm. The apparatus may include an extractor configured to extract acoustic model-state level information corresponding to an input speech signal from a training data model set using at least one of a first feature vector based on a gammatone filterbank signal analysis algorithm and a second feature vector based on a bottleneck algorithm, and a speech recognizer configured to provide a result of recognizing the input speech signal based on the extracted acoustic model-state level information.
Abstract translation: 提供了一种基于上下文相关深度神经网络隐马尔可夫模型（CD-DNN-HMM）算法的大词汇连续语音识别（LVCSR）装置。 该装置可以包括提取器，其被配置为使用基于伽马一滤波器组信号分析算法和基于第二特征向量的第一特征向量中的至少一个从训练数据模型集中提取与输入语音信号相对应的声学模型状态级别信息 以及语音识别器，被配置为基于所提取的声学模型状态级别信息来提供识别输入语音信号的结果。
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2.发明申请公开(公告)号：US20180166071A1
公开(公告)日：2018-06-14
申请号：US15607880
申请日：2017-05-30
Inventor： Sung Joo LEE , Jeon Gue PARK , Yun Keun LEE , Hoon CHUNG
Abstract: Provided are a method of automatically classifying a speaking rate and a speech recognition system using the method. The speech recognition system using automatic speaking rate classification includes a speech recognizer configured to extract word lattice information by performing speech recognition on an input speech signal, a speaking rate estimator configured to estimate word-specific speaking rates using the word lattice information, a speaking rate normalizer configured to normalize a word-specific speaking rate into a normal speaking rate when the word-specific speaking rate deviates from a preset range, and a rescoring section configured to rescore the speech signal whose speaking rate has been normalized.
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公开(公告)号：US20180165578A1
公开(公告)日：2018-06-14
申请号：US15478342
申请日：2017-04-04
Inventor： Hoon CHUNG , Jeon Gue PARK , Sung Joo LEE , Yun Keun LEE
CPC classification number: G06N3/04 , G06N3/0481 , G06N3/063
Abstract: Provided are an apparatus and method for compressing a deep neural network (DNN). The DNN compression method includes receiving a matrix of a hidden layer or an output layer of a DNN, calculating a matrix representing a nonlinear structure of the hidden layer or the output layer, and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure.
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4.发明申请SIGNAL PROCESSING ALGORITHM-INTEGRATED DEEP NEURAL NETWORK-BASED SPEECH RECOGNITION APPARATUS AND LEARNING METHOD THEREOF 审中-公开信号处理算法综合深度基于神经网络的语音识别装置及其学习方法公开(公告)号：US20160078863A1
公开(公告)日：2016-03-17
申请号：US14737907
申请日：2015-06-12
Inventor： Hoon CHUNG , Jeon Gue PARK , Sung Joo LEE , Yun Keun LEE
IPC: G10L15/16
CPC classification number: G10L15/16
Abstract: Provided are a signal processing algorithm-integrated deep neural network (DNN)-based speech recognition apparatus and a learning method thereof. A model parameter learning method in a deep neural network (DNN)-based speech recognition apparatus implementable by a computer includes converting a signal processing algorithm for extracting a feature parameter from a speech input signal of a time domain into signal processing deep neural network (DNN), fusing the signal processing DNN and a classification DNN, and learning a model parameter in a deep learning model in which the signal processing DNN and the classification DNN are fused.
Abstract translation: 提供了一种基于信号处理算法的深度神经网络（DNN）语音识别装置及其学习方法。 由计算机实现的基于深神经网络（DNN）的语音识别装置中的模型参数学习方法包括：将来自时域的语音输入信号的特征参数的信号处理算法转换为信号处理深层神经网络（DNN ），融合信号处理DNN和分类DNN，并在信号处理DNN和分类DNN融合的深度学习模型中学习模型参数。
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