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Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

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China Communications 2017年第9期14卷 23-31页

作者： Donghyun Lee minkyu lim Hosung Park Yoseb Kang Jeong-Sik Park Gil-Jin Jang Ji-Hwan Kim Department of Computer Science and Engineering Sogang University Department of Information and Communication Engineering Yeungnam University School of Electronics Engineering Kyungpook National University

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

关键词： acoustic model connectionisttemporal classification large-scale trainingcorpus long short-term memory recurrentneural network

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基于辣椒基因组测序探究辣味在种群中的进化

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辣椒杂志 2014年第2期12卷 40-50页

作者： Seungill Kim minkyu Park Seon-In Yeom Yong-Min Kim Je Min Lee Hyun-Ah Lee Eunyoung Seo Jaeyoung Choi Kyeongchae Cheong Ki-Tae Kim Kyongyong Jung Gir-Won Lee Sang-Keun Oh Chungyun Bae Saet-Byul Kim Hye-Young Lee Shin-Young Kim Myung-Shin Kim Byoung-Cheorl Kang Yeong Deuk Jo Hee-Bum Yang Hee-Jin Jeong Won-Hee Kang Jin-Kyung Kwon Chanseok Shin Jae Yun lim June Hyun Park Jin Hoe Huh June-Sik Kim Byung-Dong Kim Oded Cohen Ilan Paran Mi Chung Suh Saet Buyl Lee Yeon-Ki Kim Younhee Shin Seung-Jae Noh Junhyung Park Young Sam Seo Suk-Yoon Kwon Hyun A Kim Jeong Mee Park Hyun-Jin Kim Sang-Bong Choi Paul W Bosland Gregory Reeves Sung-Hwan Jo Bong-Woo Lee Hyung-Taeg Cho Hee-Seung Choi Min-Soo Lee Yeisoo Yu Yang Do Choi Beom-Seok Park Allen van Deynze Hamid Ashrafi Theresa Hill Woo Taek Kim Hyun-Sook Pai Hee Kyung Ahn Inhwa Yeam James J Giovannoni Jocelyn K C Rose Iben Sφrensen Sang-Jik Lee Ryan W Kim Ik-Young Choi Beom-Soon Choi Jong-Sung lim Yong-Hwan Lee Doil Choi 刘峰张西露 Department of Plant Science Seoul National University Plant Genomics and Breeding Institute Seoul National University Department of Agricultural Biotechnology Seoul National University Department of Bioinformatics and Life Science Soongsil University Vegetable Breeding Research Center Seoul National University Agricultural Research Organization Institute of Plant Science Volcani Center Bet DaganIsrael. Agricultural Research Organization Institute of Plant Science Volcani Center Bet DaganIsrael Department of Bioenergy Science and Technology Chonnam National University Genomics Genetics Institute GreenGene BioTech Inc. Codes Division Insilicogen Inc. Ginseng Resources Research Laboratory Korea Ginseng Corporation Korea Research Institute of Bioscience and Biotechnology Division of Bioscience and Bioinformatics Myongji University Department of Plant and Environmental Sciences New Mexico State University Chile Pepper Institute New Mexico State University Seeders Inc. Department of Biological Sciences Seoul National University Arizona Genomics Institute University of Arizona Agricultural Genome Center National Academy of Agricultural Science Rural Development Administration Seed Biotechnology Center University of California Davis Davis California USA Department of Systems Biology Yonsei University Department of Horticulture and Breeding Andong National University US Department of Agriculture-Agricultural Research Service Robert W. Holley Center Ithaca New York USA Boyce Thompson Institute for Plant Research Cornell University Ithaca New York USA Department of Plant Biology Cornell University Ithaca New York USA Biotechnology Institute Nongwoo Bio Yeoju Korea Genome Center University of California Davis Davis California USA National Instrumentation Center for Environmental Management Seoul National University Seoul Korea 湖南省蔬菜研究所

辣椒(Capsicum annuum)是美洲最古老的驯化作物之一,在全世界被广泛种植。本研究对CM334(墨西哥地方品种)进行了全基因组测序和组装(覆盖度为186.6×),同时还对另外两个栽培种和一个野生种分别进行了重测序和从头(de novo)测序。结果表... 详细信息

辣椒(Capsicum annuum)是美洲最古老的驯化作物之一,在全世界被广泛种植。本研究对CM334(墨西哥地方品种)进行了全基因组测序和组装(覆盖度为186.6×),同时还对另外两个栽培种和一个野生种分别进行了重测序和从头(de novo)测序。结果表明辣椒基因组大小约为番茄的4倍,辣椒基因组上存在大量gypsy转座子和花椰菜病毒家族元件的积累。结合转录组综合分析认为,辣椒素合成酶基因表达模式的改变及新功能化是导致辣椒素生物合成的原因,研究还发现辣椒与番茄在乙烯合成及果实成熟调控方面存在明显不同的分子模式。辣椒参考基因组的发布为辣椒营养品质和药用价值的改善提供了重要平台。

关键词：基因组测序辣椒素参考基因 Capsicum 转座子辣椒果实分子模式生物合成基因表达模式基因组大小

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