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公开(公告)号：US09288301B2
公开(公告)日：2016-03-15
申请号：US14227354
申请日：2014-03-27
发明人： Eui-Sok Chung , Yun-Keun Lee , Jeon-Gue Park , Ho-Young Jung , Hoon Chung
CPC分类号： H04M1/7253 , G04G21/04 , G06F1/163 , H04B1/385 , H04B2001/3861 , H04M1/57
摘要： A smart watch in accordance with an embodiment of the present invention comprises: a first smart member configured to receive a voice signal sent from a mobile terminal, transform the input voice of a user to a voice signal, and send the voice signal to the mobile terminal while in talk mode; and a second smart member configured to input a control command about the talk mode into the first smart member, and transform the voice signal to voice and output the voice.
摘要翻译： 根据本发明的实施例的智能手表包括：第一智能构件，被配置为接收从移动终端发送的语音信号，将用户的输入语音转换成语音信号，并将语音信号发送到移动台 终端在通话模式; 以及第二智能构件，被配置为将关于所述通话模式的控制命令输入到所述第一智能构件，并且将所述语音信号转换为语音并输出所述语音。
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公开(公告)号：US20150012274A1
公开(公告)日：2015-01-08
申请号：US14278485
申请日：2014-05-15
发明人： Sung-Joo LEE , Byung-Ok Kang , Hoon Chung , Ho-Young Jung , Hwa-Jeon Song , Yoo-Rhee Oh , Yun-Keun Lee
CPC分类号： G10L15/02
摘要： An apparatus for extracting features for speech recognition in accordance with the present invention includes: a frame forming portion configured to separate input speech signals in frame units having a prescribed size; a static feature extracting portion configured to extract a static feature vector for each frame of the speech signals; a dynamic feature extracting portion configured to extract a dynamic feature vector representing a temporal variance of the extracted static feature vector by use of a basis function or a basis vector; and a feature vector combining portion configured to combine the extracted static feature vector with the extracted dynamic feature vector to configure a feature vector stream.
摘要翻译： 根据本发明的用于提取用于语音识别的特征的装置包括：帧形成部分，被配置为以具有规定大小的帧单位分离输入语音信号; 静态特征提取部分，被配置为提取所述语音信号的每个帧的静态特征向量; 动态特征提取部，被配置为通过使用基函数或基矢量来提取表示所提取的静态特征向量的时间方差的动态特征向量; 以及特征向量组合部分，被配置为将所提取的静态特征向量与所提取的动态特征向量组合以配置特征向量流。
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公开(公告)号：US09799350B2
公开(公告)日：2017-10-24
申请号：US15186286
申请日：2016-06-17
发明人： Jeom Ja Kang , Hwa Jeon Song , Jeon Gue Park , Hoon Chung
IPC分类号： G10L15/00 , G10L25/87 , G10L15/197 , G10L15/02 , G10L15/18
CPC分类号： G10L25/87 , G10L15/02 , G10L15/1815 , G10L15/197 , G10L15/22 , G10L25/48
摘要： An apparatus and method for verifying an utterance based on multi-event detection information in a natural language speech recognition system. The apparatus includes a noise processor configured to process noise of an input speech signal, a feature extractor configured to extract features of speech data obtained through the noise processing, an event detector configured to detect events of the plurality of speech features occurring in the speech data using the noise-processed data and data of the extracted features, a decoder configured to perform speech recognition using a plurality of preset speech recognition models for the extracted feature data, and an utterance verifier configured to calculate confidence measurement values in units of words and sentences using information on the plurality of events detected by the event detector and a preset utterance verification model and perform utterance verification according to the calculated confidence measurement values.
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公开(公告)号：US09805716B2
公开(公告)日：2017-10-31
申请号：US15042309
申请日：2016-02-12
发明人： Sung Joo Lee , Byung Ok Kang , Jeon Gue Park , Yun Keun Lee , Hoon Chung
CPC分类号： G10L15/142 , G10L15/063 , G10L15/16 , G10L21/02
摘要： 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.
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公开(公告)号：US10789332B2
公开(公告)日：2020-09-29
申请号：US16121836
申请日：2018-09-05
发明人： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
摘要： Provided are an apparatus and method for linearly approximating a deep neural network (DNN) model which is a non-linear function. In general, a DNN model shows good performance in generation or classification tasks. However, the DNN fundamentally has non-linear characteristics, and therefore it is difficult to interpret how a result from inputs given to a black box model has been derived. To solve this problem, linear approximation of a DNN is proposed. The method for linearly approximating a DNN model includes 1) converting a neuron constituting a DNN into a polynomial, and 2) classifying the obtained polynomial as a polynomial of input signals and a polynomial of weights.
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公开(公告)号：US20190272309A1
公开(公告)日：2019-09-05
申请号：US16121836
申请日：2018-09-05
发明人： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
摘要： Provided are an apparatus and method for linearly approximating a deep neural network (DNN) model which is a non-linear function. In general, a DNN model shows good performance in generation or classification tasks. However, the DNN fundamentally has non-linear characteristics, and therefore it is difficult to interpret how a result from inputs given to a black box model has been derived. To solve this problem, linear approximation of a DNN is proposed. The method for linearly approximating a DNN model includes 1) converting a neuron constituting a DNN into a polynomial, and 2) classifying the obtained polynomial as a polynomial of input signals and a polynomial of weights.
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公开(公告)号：US10089979B2
公开(公告)日：2018-10-02
申请号：US14737907
申请日：2015-06-12
发明人： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
摘要： 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.
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8.发明申请公开(公告)号：US20130325475A1
公开(公告)日：2013-12-05
申请号：US13870409
申请日：2013-04-25
发明人： Hoon Chung , Ki-Young Park , Sung-Joo Lee , Yun-Keun Lee
IPC分类号： G10L15/05
摘要： An apparatus for detecting an end point using decoding information includes: an end point detector configured to extract a speech signal from an acoustic signal received from outside and detect end points of the speech signal; a decoder configured to decode the speech signal; and an end point detector configured to extract reference information serving as a standard of actual end point discrimination from decoding information generated during the decoding process of the decoder, and discriminate an actual end point among the end points detected by the end point detector based on the extracted reference information.
摘要翻译： 一种使用解码信息检测终端的装置，包括：端点检测器，被配置为从外部接收的声信号中提取语音信号，并检测语音信号的端点; 解码器，被配置为对所述语音信号进行解码; 以及终点检测器，其被配置为从解码器的解码处理中生成的解码信息中提取用作实际终点识别标准的参考信息，并且基于所述终点检测器检测到的终点中的实际终点， 提取参考信息。
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