Frontiers of Data and Computing ›› 2019, Vol. 1 ›› Issue (2): 26-36.

doi: 10.11871/jfdc.issn.2096-742X.2019.02.003

Special Issue: “人工智能”专刊

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The Research Development and Challenge of Automatic Speech Recognition

Liu Qingfeng,Gao Jianqing*(),Wan Genshun   

  1. IFLYTEK, Hefei, Anhui 230088, China
  • Received:2019-09-17 Online:2019-12-20 Published:2020-01-15
  • Contact: Gao Jianqing


[Objective] This paper firstly introduces the start-of-art technical framework and main challenges of Automatic Speech Recognition (ASR) systems, then provides reference for further research in the field of ASR. [Methods] Firstly, the newest framework of end-to-end speech recognition is introduced, including the Connectionist Temporal Classification(CTC) and attention based framework. Secondly, four challenging problems in ASR applications are presented, including the recognition of noisy and distant field speech, the recognition of code-switching, the recognition of domain related terms, and minority language speech recognition with limited resources. [Results] For the problem of robustness of end-to-end ASR system, an improved enhancement method and filtering attention mechanism is proposed. The start-of-art methods and future development directions are discussed regarding to the challenging problems of ASR systems. [Conclusions] There is a major challenge for the commercialization of the end-to-end ASR systems, and the research on four challenging problems plays a key role in the application of ASR systems.

Key words: automatic speech recognition, end-to-end, distant filed speech, code-switch, domain related terms