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 |
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
Fingerprint Dive into the research topics of 'Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM'. Together they form a unique fingerprint.
Cite this
Hori, T., Watanabe, S., Zhang, Y., & Chan, W. (2017). Advances in joint CTC-attention based end-to-end speech recognition with a deep CNN encoder and RNN-LM. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2017-August, 949-953. https://doi.org/10.21437/Interspeech.2017-1296