Advances in Joint CTC-Attention Based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM
TL
TL;DR
This work learns to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network and beats out traditional hybrid ASR systems on spontaneous Japanese and Chinese speech.
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.
References29 items
1
Latent Sequence Decompositions
2
Very deep convolutional networks for end-to-end speech recognition
3
Joint CTC-attention based end-to-end speech recognition using multi-task learning
4
Maximum a posteriori Based Decoding for CTC Acoustic Models
5