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公开(公告)号：US20230061505A1
公开(公告)日：2023-03-02
申请号：US17885936
申请日：2022-08-11
Inventor： Yoo Rhee OH , Ki Young PARK , Jeon Gue PARK
IPC: G10L15/06 , G10L15/30 , G06F40/166 , G10L15/02
Abstract: The present invention relates to a method of training data augmentation for end-to-end speech recognition. The method for training data augmentation for end-to-end speech recognition includes: combining speech augmentation data and text augmentation data; performing a dynamic augmentation process on each of the speech augmentation data and the text augmentation data that have been combined; and training the end-to-end speech recognition using the speech augmentation data and the text augmentation data that are subjected to the dynamic augmentation process.
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公开(公告)号：US20160240190A1
公开(公告)日：2016-08-18
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.
Abstract translation: 提供了一种基于上下文相关深度神经网络隐马尔可夫模型（CD-DNN-HMM）算法的大词汇连续语音识别（LVCSR）装置。 该装置可以包括提取器，其被配置为使用基于伽马一滤波器组信号分析算法和基于第二特征向量的第一特征向量中的至少一个从训练数据模型集中提取与输入语音信号相对应的声学模型状态级别信息 以及语音识别器，被配置为基于所提取的声学模型状态级别信息来提供识别输入语音信号的结果。
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3.发明申请公开(公告)号：US20150334443A1
公开(公告)日：2015-11-19
申请号：US14612394
申请日：2015-02-03
Inventor： Jeon Gue PARK , Eui Sok CHUNG
IPC: H04N21/422 , G10L15/22 , H04N21/482
CPC classification number: G10L15/22 , G10L2015/223 , H04N21/42203 , H04N21/42204 , H04N21/4828
Abstract: 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.
Abstract translation: 一种使用智能遥控器的语音识别广播装置及其控制方法，所述方法包括从语音识别服务器接收用于语音识别的运行时间资源; 从智能遥控器接收语音信号; 基于所接收的用于语音识别的运行时资源识别语音信号; 将所述语音信号的识别结果发送给所述智能遥控器; 从智能遥控器接收基于识别结果的EPG（电子节目指南）搜索信息或语音识别广播设备的控制信息中的至少一个; 并根据EPG搜索信息或语音识别广播设备的控制信息输出搜索屏幕或控制语音识别广播设备。
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公开(公告)号：US20230186154A1
公开(公告)日：2023-06-15
申请号：US17893628
申请日：2022-08-23
Inventor： Byunghyun YOO , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeongmin YANG , Sungwon YI , Euisok CHUNG , Ran HAN
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: An exploration method used by an exploration apparatus in multi-agent reinforcement learning to collect training samples during the training process is provided. The exploration method includes calculating the influence of a selected action of each agent on the actions of other agents in a current state, calculating a linear sum of the value of a utility function representing the action value of each agent and the influence on the actions of the other agent calculated for the selected action of each agent, and obtaining a sample to be used for training an action policy of each agent by probabilistically selecting the action in which the linear sum is the maximum, and the random action.
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公开(公告)号：US20230087477A1
公开(公告)日：2023-03-23
申请号：US17871838
申请日：2022-07-22
Inventor： Woo Yong CHOI , Jeon Gue PARK
IPC: G10L21/0272 , G10L21/0208 , G10L25/93
Abstract: The present disclosure relates to an apparatus and method for separating voice sections from each other. Various embodiments are directed to providing an apparatus and method for separating voice sections from each other, which can maximize speaker separation performance for a short voice section by dividing a short voice section having low speaker separation reliability and separating multiple speakers from one another.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20220180071A1
公开(公告)日：2022-06-09
申请号：US17540768
申请日：2021-12-02
Inventor： Eui Sok CHUNG , Hyun Woo KIM , Gyeong Moon PARK , Jeon Gue PARK , Hwa Jeon SONG , Byung Hyun YOO , Ran HAN
IPC: G06F40/40 , G06F40/284 , G06F40/216
Abstract: Provided are a system and method for adaptive masking and non-directional language understanding and generation. The system for adaptive masking and non-directional language understanding and generation according to the present invention includes an encoder unit including an adaptive masking block for performing masking on training data, a language generator for restoring masked words, and an encoder for detecting whether or not the restored sentence construction words are original, and a decoder unit including a generation word position detector for detecting a position of a word to be generated next, a language generator for determining a word suitable for the corresponding position, and a non-directional training data generator for decoder training.
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7.发明申请公开(公告)号：US20200219166A1
公开(公告)日：2020-07-09
申请号：US16711934
申请日：2019-12-12
Inventor： Hyun Woo KIM , Hwa Jeon SONG , Eui Sok CHUNG , Ho Young JUNG , Jeon Gue PARK , Yun Keun LEE
Abstract: A method and apparatus for estimating a user's requirement through a neural network which are capable of reading and writing a working memory and for providing fashion coordination knowledge appropriate for the requirement through the neural network using a long-term memory, by using the neural network using an explicit memory, in order to accurately provide the fashion coordination knowledge. The apparatus includes a language embedding unit for embedding a user's question and a previously created answer to acquire a digitized embedding vector; a fashion coordination knowledge creation unit for creating fashion coordination through the neural network having the explicit memory by using the embedding vector as an input; and a dialog creation unit for creating dialog content for configuring the fashion coordination through the neural network having the explicit memory by using the fashion coordination knowledge and the embedding vector an input.
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公开(公告)号：US20200175119A1
公开(公告)日：2020-06-04
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
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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9.发明公开公开(公告)号：US20240160859A1
公开(公告)日：2024-05-16
申请号：US18507953
申请日：2023-11-13
Inventor： Eui Sok CHUNG , Hyun Woo KIM , Jeon Gue PARK , Hwa Jeon SONG , Jeong Min YANG , Byung Hyun YOO , Ran HAN
IPC: G06F40/40
CPC classification number: G06F40/40
Abstract: The present invention relates to a multi-modality system for recommending multiple items using an interaction and a method of operating the same. The multi-modality system includes an interaction data preprocessing module that preprocesses an interaction data set and converts the preprocessed interaction data set into interaction training data; an item data preprocessing module that preprocesses item information data and converts the preprocessed item information data into item training data; and a learning module that includes a neural network model that is trained using the interaction training data and the item training data and outputs a result including a set of recommended items using a conversation context with a user as input.
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公开(公告)号：US20230274127A1
公开(公告)日：2023-08-31
申请号：US18088428
申请日：2022-12-23
Inventor： Hyun Woo KIM , Jeon Gue PARK , Hwajeon SONG , Jeongmin YANG , Byunghyun YOO , Euisok CHUNG , Ran HAN
IPC: G06N3/045 , G06F18/15 , G06F18/213 , G06F18/22
CPC classification number: G06N3/045 , G06F18/15 , G06F18/213 , G06F18/22
Abstract: A concept based few-shot learning method is disclosed. The method includes estimating a task embedding corresponding to a task to be executed from support data that is a small amount of learning data; calculating a slot probability of a concept memory necessary for a task based on the task embedding; extracting features of query data that is test data, and of the support data; comparing local features for the extracted features with slots of a concept memory to extract a concept, and generating synthesis features to have maximum similarity to the extracted features through the slots of the concept memory; and calculating a task execution result from the synthesis feature and the extracted concept by applying the slot probability as a weight.
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