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公开(公告)号：US11526732B2
公开(公告)日：2022-12-13
申请号：US16260637
申请日：2019-01-29
Inventor： Hyun Woo Kim , Ho Young Jung , Jeon Gue Park , Yun Keun Lee
Abstract: Provided are an apparatus and method for a statistical memory network. The apparatus includes a stochastic memory, an uncertainty estimator configured to estimate uncertainty information of external input signals from the input signals and provide the uncertainty information of the input signals, a writing controller configured to generate parameters for writing in the stochastic memory using the external input signals and the uncertainty information and generate additional statistics by converting statistics of the external input signals, a writing probability calculator configured to calculate a probability of a writing position of the stochastic memory using the parameters for writing, and a statistic updater configured to update stochastic values composed of an average and a variance of signals in the stochastic memory using the probability of a writing position, the parameters for writing, and the additional statistics.
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公开(公告)号：US10929612B2
公开(公告)日：2021-02-23
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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公开(公告)号：US10388275B2
公开(公告)日：2019-08-20
申请号：US15697923
申请日：2017-09-07
Inventor： Hyun Woo Kim , Ho Young Jung , Jeon Gue Park , Yun Keun Lee
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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公开(公告)号：US10402494B2
公开(公告)日：2019-09-03
申请号：US15439416
申请日：2017-02-22
Inventor： Eui Sok Chung , Byung Ok Kang , Ki Young Park , Jeon Gue Park , Hwa Jeon Song , Sung Joo Lee , Yun Keun Lee , Hyung Bae Jeon
Abstract: Provided is a method of automatically expanding input text. The method includes receiving input text composed of a plurality of documents, extracting a sentence pair that is present in different documents among the plurality of documents, setting the extracted sentence pair as an input of an encoder of a sequence-to-sequence model, setting an output of the encoder as an output of a decoder of the sequence-to-sequence model and generating a sentence corresponding to the input, and generating expanded text based on the generated sentence.
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公开(公告)号：US20180157640A1
公开(公告)日：2018-06-07
申请号：US15439416
申请日：2017-02-22
Inventor： Eui Sok CHUNG , Byung Ok Kang , Ki Young Park , Jeon Gue Park , Hwa Jeon Song , Sung Joo Lee , Yun Keun Lee , Hyung Bae Jeon
IPC: G06F17/27
CPC classification number: G06F17/2775 , G06F17/2881
Abstract: Provided is a method of automatically expanding input text. The method includes receiving input text composed of a plurality of documents, extracting a sentence pair that is present in different documents among the plurality of documents, setting the extracted sentence pair as an input of an encoder of a sequence-to-sequence model, setting an output of the encoder as an output of a decoder of the sequence-to-sequence model and generating a sentence corresponding to the input, and generating expanded text based on the generated sentence.
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Patent Agency Ranking
公开(公告)号：US09799350B2
公开(公告)日：2017-10-24
申请号：US15186286
申请日：2016-06-17
Inventor： Jeom Ja Kang , Hwa Jeon Song , Jeon Gue Park , Hoon Chung
IPC: G10L15/00 , G10L25/87 , G10L15/197 , G10L15/02 , G10L15/18
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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公开(公告)号：US10789332B2
公开(公告)日：2020-09-29
申请号：US16121836
申请日：2018-09-05
Inventor： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
Abstract: 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
Inventor： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
Abstract: 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
Inventor： Hoon Chung , Jeon Gue Park , Sung Joo Lee , Yun Keun Lee
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.
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公开(公告)号：US09799331B2
公开(公告)日：2017-10-24
申请号：US15074579
申请日：2016-03-18
Inventor： Hyun Woo Kim , Ho Young Jung , Jeon Gue Park , Yun Keun Lee
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.
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