An Overview of End-to-End Automatic Speech Recognition

• Published in: Symmetry (Basel) Vol. 11; no. 8; p. 1018
• Main Authors: Wang, Dong, Wang, Xiaodong, Lv, Shaohe
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2019
• Subjects: Acoustics; Alignment; Alliances; Artificial neural networks; Automatic speech recognition; Continuous speech; Deep learning; Hypotheses; Laboratories; Machine learning; Markov analysis; Markov chains; Neural networks; Pattern recognition; Recurrent neural networks; Segmentation; Speech; Speech recognition; Training; Voice recognition
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym11081018

Automatic speech recognition, especially large vocabulary continuous speech recognition, is an important issue in the field of machine learning. For a long time, the hidden Markov model (HMM)-Gaussian mixed model (GMM) has been the mainstream speech recognition framework. But recently, HMM-deep neural network (DNN) model and the end-to-end model using deep learning has achieved performance beyond HMM-GMM. Both using deep learning techniques, these two models have comparable performances. However, the HMM-DNN model itself is limited by various unfavorable factors such as data forced segmentation alignment, independent hypothesis, and multi-module individual training inherited from HMM, while the end-to-end model has a simplified model, joint training, direct output, no need to force data alignment and other advantages. Therefore, the end-to-end model is an important research direction of speech recognition. In this paper we review the development of end-to-end model. This paper first introduces the basic ideas, advantages and disadvantages of HMM-based model and end-to-end models, and points out that end-to-end model is the development direction of speech recognition. Then the article focuses on the principles, progress and research hotspots of three different end-to-end models, which are connectionist temporal classification (CTC)-based, recurrent neural network (RNN)-transducer and attention-based, and makes theoretically and experimentally detailed comparisons. Their respective advantages and disadvantages and the possible future development of the end-to-end model are finally pointed out. Automatic speech recognition is a pattern recognition task in the field of computer science, which is a subject area of Symmetry.