Abstract
We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.
| Original language | English |
|---|---|
| Pages (from-to) | 949-953 |
| Number of pages | 5 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| Volume | 2017-August |
| DOIs | |
| Publication status | Published - 2017 Jan 1 |
| Externally published | Yes |
| Event | 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden Duration: 2017 Aug 20 → 2017 Aug 24 |
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Keywords
- Attention model
- Connectionist temporal classification
- Encoder-decoder
- End-to-end speech recognition
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modelling and Simulation
Cite this
Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM. / Hori, Takaaki; Watanabe, Shinji; Zhang, Yu; Chan, William.
In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, Vol. 2017-August, 01.01.2017, p. 949-953.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM
AU - Hori, Takaaki
AU - Watanabe, Shinji
AU - Zhang, Yu
AU - Chan, William
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.
AB - We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.
KW - Attention model
KW - Connectionist temporal classification
KW - Encoder-decoder
KW - End-to-end speech recognition
UR - http://www.scopus.com/inward/record.url?scp=85039169903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85039169903&partnerID=8YFLogxK
U2 - 10.21437/Interspeech.2017-1296
DO - 10.21437/Interspeech.2017-1296
M3 - Conference article
AN - SCOPUS:85039169903
VL - 2017-August
SP - 949
EP - 953
JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
SN - 2308-457X
ER -