Proceedings ArticleDOI
The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices
Takuya Yoshioka,Nobutaka Ito,Marc Delcroix,Atsunori Ogawa,Keisuke Kinoshita,Masakiyo Fujimoto,Chengzhu Yu,Wojciech J. Fabian,Miquel Espi,Takuya Higuchi,Shoko Araki,Tomohiro Nakatani +11 more
- pp 436-443
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TLDR
NTT's CHiME-3 system is described, which integrates advanced speech enhancement and recognition techniques, which achieves a 3.45% development error rate and a 5.83% evaluation error rate.Abstract:
CHiME-3 is a research community challenge organised in 2015 to evaluate speech recognition systems for mobile multi-microphone devices used in noisy daily environments. This paper describes NTT's CHiME-3 system, which integrates advanced speech enhancement and recognition techniques. Newly developed techniques include the use of spectral masks for acoustic beam-steering vector estimation and acoustic modelling with deep convolutional neural networks based on the "network in network" concept. In addition to these improvements, our system has several key differences from the official baseline system. The differences include multi-microphone training, dereverberation, and cross adaptation of neural networks with different architectures. The impacts that these techniques have on recognition performance are investigated. By combining these advanced techniques, our system achieves a 3.45% development error rate and a 5.83% evaluation error rate. Three simpler systems are also developed to perform evaluations with constrained set-ups.read more
Citations
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End to end speech recognition in English and Mandarin
Dario Amodei,Rishita Anubhai,Eric Battenberg,Carl Case,Jared Casper,Bryan Catanzaro,Jingdong Chen,Mike Chrzanowski,Adam Coates,Greg Diamos,Erich Elsen,Jesse Engel,Linxi Fan,Christopher Fougner,Tony X. Han,Awni Hannun,Billy Jun,Patrick LeGresley,Libby Lin,Sharan Narang,Andrew Y. Ng,Sherjil Ozair,Ryan Prenger,Jonathan Raiman,Sanjeev Satheesh,David Seetapun,Shubho Sengupta,Yi Wang,Zhiqian Wang,Chong Wang,Bo Xiao,Dani Yogatama,Jun Zhan,Zhenyao Zhu +33 more
TL;DR: It is shown that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech-two vastly different languages, and is competitive with the transcription of human workers when benchmarked on standard datasets.
Proceedings Article
Deep speech 2: end-to-end speech recognition in English and mandarin
Dario Amodei,Sundaram Ananthanarayanan,Rishita Anubhai,Jingliang Bai,Eric Battenberg,Carl Case,Jared Casper,Bryan Catanzaro,Qiang Cheng,Guoliang Chen,Jie Chen,Jingdong Chen,Zhijie Chen,Mike Chrzanowski,Adam Coates,Greg Diamos,Ke Ding,Niandong Du,Erich Elsen,Jesse Engel,Weiwei Fang,Linxi Fan,Christopher Fougner,Liang Gao,Caixia Gong,Awni Hannun,Tony X. Han,Lappi Vaino Johannes,Bing Jiang,Cai Ju,Billy Jun,Patrick LeGresley,Libby Lin,Junjie Liu,Yang Liu,Weigao Li,Xiangang Li,Dongpeng Ma,Sharan Narang,Andrew Y. Ng,Sherjil Ozair,Yiping Peng,Ryan Prenger,Sheng Qian,Zongfeng Quan,Jonathan Raiman,Vinay Rao,Sanjeev Satheesh,David Seetapun,Shubho Sengupta,Kavya Srinet,Anuroop Sriram,Haiyuan Tang,Liliang Tang,Chong Wang,Jidong Wang,Kaifu Wang,Yi Wang,Zhijian Wang,Zhiqian Wang,Shuang Wu,Likai Wei,Bo Xiao,Wen Xie,Yan Xie,Dani Yogatama,Bin Yuan,Jun Zhan,Zhenyao Zhu +68 more
TL;DR: In this article, an end-to-end deep learning approach was used to recognize either English or Mandarin Chinese speech-two vastly different languages-using HPC techniques, enabling experiments that previously took weeks to now run in days.
Journal ArticleDOI
A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
Journal ArticleDOI
Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang,Jitong Chen +1 more
TL;DR: A comprehensive overview of deep learning-based supervised speech separation can be found in this paper, where three main components of supervised separation are discussed: learning machines, training targets, and acoustic features.
Journal ArticleDOI
Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
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