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公开(公告)号：US20250124914A1
公开(公告)日：2025-04-17
申请号：US18734064
申请日：2024-06-05
Inventor： Byung Ok Kang , Yoonhyung Kim , Hwajeon Song , HOON CHUNG
IPC: G10L15/06
Abstract: Provided is a method of training a speech recognizer based on shared and exclusive attributes. The method includes: inputting a parallel speech corpus constituting a labeled speech corpus and a non-parallel speech corpus into a speech encoder constituting a speech recognizer; outputting a representation vector representing training speech as an output of the speech encoder; inputting a parallel text corpus constituting the labeled speech corpus and a non-parallel text corpus into a text encoder; outputting a representation vector representing text as an output of the text encoder; and receiving and decoding, by a decoder, each of the representation vectors of the speech encoder and the text encoder.
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Patent Agency Ranking
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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公开(公告)号：US11423238B2
公开(公告)日：2022-08-23
申请号：US16671773
申请日：2019-11-01
Inventor： Eui Sok Chung , Hyun Woo Kim , Hwa Jeon Song , Ho Young Jung , Byung Ok Kang , Jeon Gue Park , Yoo Rhee Oh , Yun Keun Lee
IPC: G06F40/56 , G06F40/30 , G06F40/289
Abstract: Provided are sentence embedding method and apparatus based on subword embedding and skip-thoughts. To integrate skip-thought sentence embedding learning methodology with a subword embedding technique, a skip-thought sentence embedding learning method based on subword embedding and methodology for simultaneously learning subword embedding learning and skip-thought sentence embedding learning, that is, multitask learning methodology, are provided as methodology for applying intra-sentence contextual information to subword embedding in the case of subword embedding learning. This makes it possible to apply a sentence embedding approach to agglutinative languages such as Korean in a bag-of-words form. Also, skip-thought sentence embedding learning methodology is integrated with a subword embedding technique such that intra-sentence contextual information can be used in the case of subword embedding learning. A proposed model minimizes additional training parameters based on sentence embedding such that most training results may be accumulated in a subword embedding parameter.
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5.发明授权公开(公告)号：US09959862B2
公开(公告)日：2018-05-01
申请号：US15187581
申请日：2016-06-20
Inventor： Byung Ok Kang , Jeon Gue Park , Hwa Jeon Song , Yun Keun Lee , Eui Sok Chung
CPC classification number: G10L15/16 , G10L15/063 , G10L15/07 , G10L2015/022 , G10L2015/0636
Abstract: 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.
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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
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.
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