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1.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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2.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：US20140129233A1
公开(公告)日：2014-05-08
申请号：US13853855
申请日：2013-03-29
Inventor： Eui Sok CHUNG , Yun Keun LEE , Hyung Bae JEON , Ho Young JUNG , Jeom Ja KANG
Abstract: Disclosed is apparatus and system for user interface. The apparatus for user interface comprises a body unit including a groove which is corresponding to a structure of an oral cavity and operable to be mounted on upper part of the oral cavity; a user input unit receiving a signal from the user's tongue in a part of the body unit; a communication unit transmitting the signal received from the user input unit; and a charging unit supplying an electrical energy generated from vibration or pressure caused by movement of the user's tongue.
Abstract translation: 公开了用于用户界面的装置和系统。 用于用户界面的装置包括主体单元，其包括对应于口腔结构并可操作以安装在口腔上部的凹槽; 用户输入单元，在身体单元的一部分中接收来自用户舌头的信号; 通信单元，发送从用户输入单元接收的信号; 以及充电单元，其提供由用户舌头的移动引起的振动或压力产生的电能。
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4.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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Patent Agency Ranking
6.发明申请公开(公告)号：US20180075023A1
公开(公告)日：2018-03-15
申请号：US15700537
申请日：2017-09-11
Inventor： Chang Hyun KIM , Young Kil KIM , Yun Keun LEE
CPC classification number: G06F17/289 , G06F3/0481 , G06F17/211 , G06F17/2775 , G10L15/04 , G10L15/10 , G10L15/16 , G10L15/18 , G10L15/26 , G10L25/78
Abstract: The present invention relates to a device of simultaneous interpretation based on real-time extraction of an interpretation unit, the device including a voice recognition module configured to recognize voice units as sentence units or translation units from vocalized speech that is input in real time, a real-time interpretation unit extraction module configured to form one or more of the voice units into an interpretation unit, and a real-time interpretation module configured to perform an interpretation task for each interpretation unit formed by the real-time interpretation unit extraction module.
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公开(公告)号：US20170213545A1
公开(公告)日：2017-07-27
申请号：US15405425
申请日：2017-01-13
Inventor： Oh Woog KWON , Young Kil KIM , Yun Keun LEE
CPC classification number: G06N20/00 , G06F17/241 , G06F17/279 , G10L15/1815 , G10L15/22
Abstract: An incremental self-learning based dialogue apparatus for dialogue knowledge includes a dialogue processing unit configured to determine a intention of a user utterance by using a knowledge base and perform processing or a response suitable for the user intention, a dialogue establishment unit configured to automatically learn a user intention stored in a intention annotated learning corpus, store information about the learned user intention in the knowledge base, and edit and manage the knowledge base and the intention annotated learning corpus, and a self-knowledge augmentation unit configured to store a log of a dialogue performed by the dialogue processing unit, detect and classify an error in the stored dialogue log, automatically tag a user intention for the detected and classified error, and store the tagged user intention in the intention annotated learning corpus.
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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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9.发明申请公开(公告)号：US20210004542A1
公开(公告)日：2021-01-07
申请号：US16919748
申请日：2020-07-02
Inventor： Seung YUN , Sang Hun KIM , Min Kyu LEE , Yun Keun LEE , Mu Yeol CHOI , Yeo Jeong KIM , Sang Kyu PARK
Abstract: Provided are an apparatus and method for providing a personal assistant service based on automatic translation. The apparatus for providing a personal assistant service based on automatic translation includes an input section configured to receive a command of a user, a memory in which a program for providing a personal assistant service according to the command of the user is stored, and a processor configured to execute the program. The processor updates at least one of a speech recognition model, an automatic interpretation model, and an automatic translation model on the basis of an intention of the command of the user using a recognition result of the command of the user and provides the personal assistant service on the basis of an automatic translation call.
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10.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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