Patents by Inventor Jeon Gue Park
Jeon Gue Park has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10388275Abstract: 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.Type: GrantFiled: September 7, 2017Date of Patent: August 20, 2019Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Hyun Woo Kim, Ho Young Jung, Jeon Gue Park, Yun Keun Lee
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Patent number: 10089979Abstract: 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.Type: GrantFiled: June 12, 2015Date of Patent: October 2, 2018Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Hoon Chung, Jeon Gue Park, Sung Joo Lee, Yun Keun Lee
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Publication number: 20180268739Abstract: 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.Type: ApplicationFiled: September 20, 2017Publication date: September 20, 2018Applicant: Electronics and Telecommunications Research InstituteInventors: Hoon CHUNG, Jeon Gue PARK, Yoo Rhee OH, Yun Kyung LEE, Yun Keun LEE
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Publication number: 20180247642Abstract: 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.Type: ApplicationFiled: September 7, 2017Publication date: August 30, 2018Applicant: Electronics and Telecommunications Research InstituteInventors: Hyun Woo KIM, Ho Young JUNG, Jeon Gue PARK, Yun Keun LEE
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Publication number: 20180165578Abstract: 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.Type: ApplicationFiled: April 4, 2017Publication date: June 14, 2018Applicant: Electronics and Telecommunications Research InstituteInventors: Hoon CHUNG, Jeon Gue PARK, Sung Joo LEE, Yun Keun LEE
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Publication number: 20180166071Abstract: 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.Type: ApplicationFiled: May 30, 2017Publication date: June 14, 2018Applicant: Electronics and Telecommunications Research InstituteInventors: Sung Joo LEE, Jeon Gue PARK, Yun Keun LEE, Hoon CHUNG
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Publication number: 20180157640Abstract: 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.Type: ApplicationFiled: February 22, 2017Publication date: June 7, 2018Applicant: Electronics and Telecommunications Research InstituteInventors: Eui Sok CHUNG, Byung Ok Kang, Ki Young Park, Jeon Gue Park, Hwa Jeon Song, Sung Joo Lee, Yun Keun Lee, Hyung Bae Jeon
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Patent number: 9959862Abstract: A speech recognition apparatus based on a deep-neural-network (DNN) sound model includes a memory and a processor. As the processor executes a program stored in the memory, the processor generates sound-model state sets corresponding to a plurality of pieces of set training speech data included in multi-set training speech data, generates a multi-set state cluster from the sound-model state sets, and sets the multi-set training speech data as an input node and the multi-set state cluster as output nodes so as to learn a DNN structured parameter.Type: GrantFiled: June 20, 2016Date of Patent: May 1, 2018Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Byung Ok Kang, Jeon Gue Park, Hwa Jeon Song, Yun Keun Lee, Eui Sok Chung
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Publication number: 20180047389Abstract: Provided are an apparatus and method for recognizing speech using an attention-based content-dependent (CD) acoustic model. The apparatus includes a predictive deep neural network (DNN) configured to receive input data from an input layer and output predictive values to a buffer of a first output layer, and a context DNN configured to receive a context window from the first output layer and output a final result value.Type: ApplicationFiled: January 12, 2017Publication date: February 15, 2018Applicant: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Hwa Jeon SONG, Byung Ok KANG, Jeon Gue PARK, Yun Keun LEE, Hyung Bae JEON, Ho Young JUNG
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Patent number: 9805716Abstract: 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.Type: GrantFiled: February 12, 2016Date of Patent: October 31, 2017Assignee: Electronics and Telecommunications Research InstituteInventors: Sung Joo Lee, Byung Ok Kang, Jeon Gue Park, Yun Keun Lee, Hoon Chung
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Patent number: 9799331Abstract: 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.Type: GrantFiled: March 18, 2016Date of Patent: October 24, 2017Assignee: Electronics and Telecommunications Research InstituteInventors: Hyun Woo Kim, Ho Young Jung, Jeon Gue Park, Yun Keun Lee
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Patent number: 9799350Abstract: 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.Type: GrantFiled: June 17, 2016Date of Patent: October 24, 2017Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Jeom Ja Kang, Hwa Jeon Song, Jeon Gue Park, Hoon Chung
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Publication number: 20170206894Abstract: A speech recognition apparatus based on a deep-neural-network (DNN) sound model includes a memory and a processor. As the processor executes a program stored in the memory, the processor generates sound-model state sets corresponding to a plurality of pieces of set training speech data included in multi-set training speech data, generates a multi-set state cluster from the sound-model state sets, and sets the multi-set training speech data as an input node and the multi-set state cluster as output nodes so as to learn a DNN structured parameter.Type: ApplicationFiled: June 20, 2016Publication date: July 20, 2017Inventors: Byung Ok KANG, Jeon Gue PARK, Hwa Jeon SONG, Yun Keun LEE, Eui Sok CHUNG
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Publication number: 20170200458Abstract: 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.Type: ApplicationFiled: June 17, 2016Publication date: July 13, 2017Inventors: Jeom Ja KANG, Hwa Jeon SONG, Jeon Gue PARK, Hoon CHUNG
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Publication number: 20160275964Abstract: 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.Type: ApplicationFiled: March 18, 2016Publication date: September 22, 2016Inventors: Hyun Woo KIM, Ho Young JUNG, Jeon Gue PARK, Yun Keun LEE
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Publication number: 20160240190Abstract: 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.Type: ApplicationFiled: February 12, 2016Publication date: August 18, 2016Inventors: Sung Joo LEE, Byung Ok KANG, Jeon Gue PARK, Yun Keun LEE, Hoon CHUNG
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Patent number: 9390426Abstract: Disclosed are a personalized advertisement device based on speech recognition SMS services and a personalized advertisement exposure method based on speech recognition SMS services. The present invention provides a personalized advertisement device based on speech recognition SMS services and a personalized advertisement exposure method based on speech recognition SMS services capable of maximizing an effect of advertisement by grasping user's intention, an emotion state, and positional information from speech data uttered by a user during a process of providing speech recognition SMS services, configuring advertisements from when speech data begins conversion to when it has been completely converted by the speech recognition into character strings, and exposing the configured advertisements to a user.Type: GrantFiled: September 5, 2012Date of Patent: July 12, 2016Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Hoon Chung, Jeon Gue Park, Hyung Bae Jeon, Ki Young Park, Yun Keun Lee, Sang Kyu Park
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Publication number: 20160078863Abstract: 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.Type: ApplicationFiled: June 12, 2015Publication date: March 17, 2016Inventors: Hoon CHUNG, Jeon Gue PARK, Sung Joo LEE, Yun Keun LEE
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Patent number: 9288301Abstract: 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.Type: GrantFiled: March 27, 2014Date of Patent: March 15, 2016Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTEInventors: Eui-Sok Chung, Yun-Keun Lee, Jeon-Gue Park, Ho-Young Jung, Hoon Chung
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Publication number: 20150334443Abstract: A speech recognition broadcasting apparatus that uses a smart remote control and a controlling method thereof, the method including receiving a runtime resource for speech recognition from a speech recognition server; receiving a speech signal from the smart remote control; recognizing the speech signal based on the received runtime resource for speech recognition; transmitting a result of recognition of the speech signal to the smart remote control; receiving at least one of EPG (Electronic Program Guide) search information or control information of the speech recognition broadcasting apparatus that are based on the result of recognition from the smart remote control; and outputting a search screen or controlling the speech recognition broadcasting apparatus based on the EPG search information or control information of the speech recognition broadcasting apparatus.Type: ApplicationFiled: February 3, 2015Publication date: November 19, 2015Applicant: Electronics and Telecommunications Research InstituteInventors: Jeon Gue PARK, Eui Sok CHUNG