Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks

Ying Zhang, Mohammad Pezeshki, Philémon Brakel, Saizheng Zhang, César Laurent, Yoshua Bengio, Aaron Courville

Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic features for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an ‘end-to-end’ speech recognition system instead of hybrid settings. However, RNNs are computationally expensive and sometimes difficult to train. In this paper, inspired by the advantages of both CNNs and the CTC approach, we propose an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with CTC directly without recurrent connections. By evaluating the approach on the TIMIT phoneme recognition task, we show that the proposed model is not only computationally efficient, but also competitive with the existing baseline systems. Moreover, we argue that CNNs have the capability to model temporal correlations with appropriate context information.

DOI: 10.21437/Interspeech.2016-1446

Cite as

Zhang, Y., Pezeshki, M., Brakel, P., Zhang, S., Laurent, C., Bengio, Y., Courville, A. (2016) Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks. Proc. Interspeech 2016, 410-414.

author={Ying Zhang and Mohammad Pezeshki and Philémon Brakel and Saizheng Zhang and César Laurent and Yoshua Bengio and Aaron Courville},
title={Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks},
booktitle={Interspeech 2016},