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 Han, Lappi Johannes, Bing Jiang, Cai Ju, Billy Jun, Patrick LeGresley, Libby Lin, Junjie Liu, Yang Liu, Weigao Li, Xiangang Li, Dongpeng Ma, Sharan Narang, Andrew 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
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:173-182, 2016.

Abstract

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Cite this Paper


BibTeX
@InProceedings{pmlr-v48-amodei16, title = {Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin}, author = {Amodei, Dario and Ananthanarayanan, Sundaram and Anubhai, Rishita and Bai, Jingliang and Battenberg, Eric and Case, Carl and Casper, Jared and Catanzaro, Bryan and Cheng, Qiang and Chen, Guoliang and Chen, Jie and Chen, Jingdong and Chen, Zhijie and Chrzanowski, Mike and Coates, Adam and Diamos, Greg and Ding, Ke and Du, Niandong and Elsen, Erich and Engel, Jesse and Fang, Weiwei and Fan, Linxi and Fougner, Christopher and Gao, Liang and Gong, Caixia and Hannun, Awni and Han, Tony and Johannes, Lappi and Jiang, Bing and Ju, Cai and Jun, Billy and LeGresley, Patrick and Lin, Libby and Liu, Junjie and Liu, Yang and Li, Weigao and Li, Xiangang and Ma, Dongpeng and Narang, Sharan and Ng, Andrew and Ozair, Sherjil and Peng, Yiping and Prenger, Ryan and Qian, Sheng and Quan, Zongfeng and Raiman, Jonathan and Rao, Vinay and Satheesh, Sanjeev and Seetapun, David and Sengupta, Shubho and Srinet, Kavya and Sriram, Anuroop and Tang, Haiyuan and Tang, Liliang and Wang, Chong and Wang, Jidong and Wang, Kaifu and Wang, Yi and Wang, Zhijian and Wang, Zhiqian and Wu, Shuang and Wei, Likai and Xiao, Bo and Xie, Wen and Xie, Yan and Yogatama, Dani and Yuan, Bin and Zhan, Jun and Zhu, Zhenyao}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {173--182}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/amodei16.pdf}, url = { http://proceedings.mlr.press/v48/amodei16.html }, abstract = {We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.} }
Endnote
%0 Conference Paper %T Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin %A Dario Amodei %A Sundaram Ananthanarayanan %A Rishita Anubhai %A Jingliang Bai %A Eric Battenberg %A Carl Case %A Jared Casper %A Bryan Catanzaro %A Qiang Cheng %A Guoliang Chen %A Jie Chen %A Jingdong Chen %A Zhijie Chen %A Mike Chrzanowski %A Adam Coates %A Greg Diamos %A Ke Ding %A Niandong Du %A Erich Elsen %A Jesse Engel %A Weiwei Fang %A Linxi Fan %A Christopher Fougner %A Liang Gao %A Caixia Gong %A Awni Hannun %A Tony Han %A Lappi Johannes %A Bing Jiang %A Cai Ju %A Billy Jun %A Patrick LeGresley %A Libby Lin %A Junjie Liu %A Yang Liu %A Weigao Li %A Xiangang Li %A Dongpeng Ma %A Sharan Narang %A Andrew Ng %A Sherjil Ozair %A Yiping Peng %A Ryan Prenger %A Sheng Qian %A Zongfeng Quan %A Jonathan Raiman %A Vinay Rao %A Sanjeev Satheesh %A David Seetapun %A Shubho Sengupta %A Kavya Srinet %A Anuroop Sriram %A Haiyuan Tang %A Liliang Tang %A Chong Wang %A Jidong Wang %A Kaifu Wang %A Yi Wang %A Zhijian Wang %A Zhiqian Wang %A Shuang Wu %A Likai Wei %A Bo Xiao %A Wen Xie %A Yan Xie %A Dani Yogatama %A Bin Yuan %A Jun Zhan %A Zhenyao Zhu %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-amodei16 %I PMLR %P 173--182 %U http://proceedings.mlr.press/v48/amodei16.html %V 48 %X We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.
RIS
TY - CPAPER TI - Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin AU - Dario Amodei AU - Sundaram Ananthanarayanan AU - Rishita Anubhai AU - Jingliang Bai AU - Eric Battenberg AU - Carl Case AU - Jared Casper AU - Bryan Catanzaro AU - Qiang Cheng AU - Guoliang Chen AU - Jie Chen AU - Jingdong Chen AU - Zhijie Chen AU - Mike Chrzanowski AU - Adam Coates AU - Greg Diamos AU - Ke Ding AU - Niandong Du AU - Erich Elsen AU - Jesse Engel AU - Weiwei Fang AU - Linxi Fan AU - Christopher Fougner AU - Liang Gao AU - Caixia Gong AU - Awni Hannun AU - Tony Han AU - Lappi Johannes AU - Bing Jiang AU - Cai Ju AU - Billy Jun AU - Patrick LeGresley AU - Libby Lin AU - Junjie Liu AU - Yang Liu AU - Weigao Li AU - Xiangang Li AU - Dongpeng Ma AU - Sharan Narang AU - Andrew Ng AU - Sherjil Ozair AU - Yiping Peng AU - Ryan Prenger AU - Sheng Qian AU - Zongfeng Quan AU - Jonathan Raiman AU - Vinay Rao AU - Sanjeev Satheesh AU - David Seetapun AU - Shubho Sengupta AU - Kavya Srinet AU - Anuroop Sriram AU - Haiyuan Tang AU - Liliang Tang AU - Chong Wang AU - Jidong Wang AU - Kaifu Wang AU - Yi Wang AU - Zhijian Wang AU - Zhiqian Wang AU - Shuang Wu AU - Likai Wei AU - Bo Xiao AU - Wen Xie AU - Yan Xie AU - Dani Yogatama AU - Bin Yuan AU - Jun Zhan AU - Zhenyao Zhu BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-amodei16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 173 EP - 182 L1 - http://proceedings.mlr.press/v48/amodei16.pdf UR - http://proceedings.mlr.press/v48/amodei16.html AB - We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale. ER -
APA
Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Cheng, Q., Chen, G., Chen, J., Chen, J., Chen, Z., Chrzanowski, M., Coates, A., Diamos, G., Ding, K., Du, N., Elsen, E., Engel, J., Fang, W., Fan, L., Fougner, C., Gao, L., Gong, C., Hannun, A., Han, T., Johannes, L., Jiang, B., Ju, C., Jun, B., LeGresley, P., Lin, L., Liu, J., Liu, Y., Li, W., Li, X., Ma, D., Narang, S., Ng, A., Ozair, S., Peng, Y., Prenger, R., Qian, S., Quan, Z., Raiman, J., Rao, V., Satheesh, S., Seetapun, D., Sengupta, S., Srinet, K., Sriram, A., Tang, H., Tang, L., Wang, C., Wang, J., Wang, K., Wang, Y., Wang, Z., Wang, Z., Wu, S., Wei, L., Xiao, B., Xie, W., Xie, Y., Yogatama, D., Yuan, B., Zhan, J. & Zhu, Z.. (2016). Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:173-182 Available from http://proceedings.mlr.press/v48/amodei16.html .

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