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公开(公告)号：US20210374545A1
公开(公告)日：2021-12-02
申请号：US17332464
申请日：2021-05-27
Inventor： Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Yoo Rhee OH , Byung Hyun YOO , Eui Sok CHUNG , Ran HAN
Abstract: A knowledge increasing method includes calculating uncertainty of knowledge obtained from a neural network using an explicit memory, determining the insufficiency of the knowledge on the basis of the calculated uncertainty, obtaining additional data (learning data) for increasing insufficient knowledge, and training the neural network by using the additional data to autonomously increase knowledge.
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公开(公告)号：US20170200458A1
公开(公告)日：2017-07-13
申请号：US15186286
申请日：2016-06-17
Inventor： Jeom Ja KANG , Hwa Jeon SONG , Jeon Gue PARK , Hoon CHUNG
IPC: G10L25/87 , G10L15/02 , G10L15/18 , G10L15/197
CPC classification number: G10L25/87 , G10L15/02 , G10L15/1815 , G10L15/197 , G10L15/22 , G10L25/48
Abstract: 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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13.发明申请FEATURE COMPENSATION APPARATUS AND METHOD FOR SPEECH RECOGNTION IN NOISY ENVIRONMENT 有权特征补偿装置和噪声环境中语音识别的方法公开(公告)号：US20160275964A1
公开(公告)日：2016-09-22
申请号：US15074579
申请日：2016-03-18
Inventor： Hyun Woo KIM , Ho Young JUNG , Jeon Gue PARK , Yun Keun LEE
IPC: G10L21/0232 , G10L15/14 , G10L19/26 , G10L21/0308 , G10L25/24
Abstract: A feature compensation apparatus includes a feature extractor configured to extract corrupt speech features from a corrupt speech signal with additive noise that consists of two or more frames; a noise estimator configured to estimate noise features based on the extracted corrupt speech features and compensated speech features; a probability calculator configured to calculate a correlation between adjacent frames of the corrupt speech signal; and a speech feature compensator configured to generate compensated speech features by eliminating noise features of the extracted corrupt speech features while taking into consideration the correlation between adjacent frames of the corrupt speech signal and the estimated noise features, and to transmit the generated compensated speech features to the noise estimator.
Abstract translation: 特征补偿装置包括特征提取器，其被配置为从具有由两个或更多个帧组成的附加噪声的损坏语音信号中提取损坏的语音特征; 噪声估计器，被配置为基于所提取的损坏的语音特征和补偿的语音特征来估计噪声特征; 概率计算器，被配置为计算所述损坏语音信号的相邻帧之间的相关性; 以及语音特征补偿器，被配置为通过消除所提取的损坏的语音特征的噪声特征来产生补偿的语音特征，同时考虑到损坏的语音信号的相邻帧与估计的噪声特征之间的相关性，并且将生成的补偿语音特征发送到 噪声估计器。
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14.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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公开(公告)号：US20190325025A1
公开(公告)日：2019-10-24
申请号：US16217804
申请日：2018-12-12
Inventor： Ho Young JUNG , Hyun Woo KIM , Hwa Jeon SONG , Eui Sok CHUNG , Jeon Gue PARK
Abstract: Provided are a neural network memory computing system and method. The neural network memory computing system includes a first processor configured to learn a sense-making process on the basis of sense-making multimodal training data stored in a database, receive multiple modalities, and output a sense-making result on the basis of results of the learning, and a second processor configured to generate a sense-making training set for the first processor to increase knowledge for learning a sense-making process and provide the generated sense-making training set to the first processor.
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Patent Agency Ranking
16.发明申请公开(公告)号：US20180268739A1
公开(公告)日：2018-09-20
申请号：US15709686
申请日：2017-09-20
Inventor： Hoon CHUNG , Jeon Gue PARK , Yoo Rhee OH , Yun Kyung LEE , Yun Keun LEE
CPC classification number: G09B19/06 , G06N3/0454 , G06N3/084 , G09B5/00 , G09B7/00 , G10L15/02 , G10L15/16 , G10L15/22 , G10L25/30 , G10L25/60
Abstract: Provided are end-to-end method and system for grading foreign language fluency, in which a multi-step intermediate process of grading foreign language fluency in the related art is omitted. The method provides an end-to-end foreign language fluency grading method of grading a foreign language fluency of a non-native speaker from a non-native raw speech signal, and includes inputting the raw speech to a convolution neural network (CNN), training a filter coefficient of the CNN based on a fluency grading score calculated by a human rater for the raw signal so as to generate a foreign language fluency grading model, and grading foreign language fluency for a non-native speech signal newly input to the trained CNN by using the foreign language fluency grading model to output a grading result.
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公开(公告)号：US20180247642A1
公开(公告)日：2018-08-30
申请号：US15697923
申请日：2017-09-07
Inventor： Hyun Woo KIM , Ho Young JUNG , Jeon Gue PARK , Yun Keun LEE
CPC classification number: G10L15/16 , G06N3/08 , G06N3/084 , G10L15/02 , G10L15/04 , G10L21/04 , G10L25/84 , G10L2015/025 , G10L2015/027
Abstract: The present invention relates to a method and apparatus for improving spontaneous speech recognition performance. The present invention is directed to providing a method and apparatus for improving spontaneous speech recognition performance by extracting a phase feature as well as a magnitude feature of a voice signal transformed to the frequency domain, detecting a syllabic nucleus on the basis of a deep neural network using a multi-frame output, determining a speaking rate by dividing the number of syllabic nuclei by a voice section interval detected by a voice detector, calculating a length variation or an overlap factor according to the speaking rate, and performing cepstrum length normalization or time scale modification with a voice length appropriate for an acoustic model.
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18.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：US20210398004A1
公开(公告)日：2021-12-23
申请号：US17353136
申请日：2021-06-21
Inventor： Hyun Woo KIM , Gyeong Moon PARK , Jeon Gue PARK , Hwa Jeon SONG , Byung Hyun YOO , Eui Sok CHUNG , Ran HAN
Abstract: Provided are a method and apparatus for online Bayesian few-shot learning. The present invention provides a method and apparatus for online Bayesian few-shot learning in which multi-domain-based online learning and few-shot learning are integrated when domains of tasks having data are sequentially given.
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20.发明申请公开(公告)号：US20200184310A1
公开(公告)日：2020-06-11
申请号：US16711317
申请日：2019-12-11
Inventor： Hoon CHUNG , Jeon Gue PARK , Yun Keun LEE
Abstract: Provided is an apparatus and method for reducing the number of deep neural network model parameters, the apparatus including a memory in which a program for DNN model parameter reduction is stored, and a processor configured to execute the program, wherein the processor represents hidden layers of the model of the DNN using a full-rank decomposed matrix, uses training that is employed with a sparsity constraint for converting a diagonal matrix value to zero, and determines a rank of each of the hidden layers of the model of the DNN according to a degree of the sparsity constraint.
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