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Past review, current progress, and challenges ahead on the cocktail party problem

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An Erratum to this article was published on 01 March 2019

An Erratum to this article was published on 01 April 2018

This article has been updated

Abstract

The cocktail party problem, i.e., tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously, is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition (ASR) systems. In this overview paper, we review the techniques proposed in the last two decades in attacking this problem. We focus our discussions on the speech separation problem given its central role in the cocktail party environment, and describe the conventional single-channel techniques such as computational auditory scene analysis (CASA), non-negative matrix factorization (NMF) and generative models, the conventional multi-channel techniques such as beamforming and multi-channel blind source separation, and the newly developed deep learning-based techniques, such as deep clustering (DPCL), the deep attractor network (DANet), and permutation invariant training (PIT). We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party environment. We argue effectively exploiting information in the microphone array, the acoustic training set, and the language itself using a more powerful model. Better optimization objective and techniques will be the approach to solving the cocktail party problem.

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Change history

  • 11 June 2018

    In the original version of this article, the affiliations are incorrect. The correct affiliations are given above.

    The corresponding author’s E-mail address should be yanminqian@sjtu.edu.cn.

  • 19 April 2019

    In the original version of this article, there is a mistake about the result of DPCL++ (Isik et al., 2016) in Section 5.6 (Fig. 7). As reported in Isik et al. (2016), the SDR improvement was 10.3 dB, rather than 9.4 dB. For further information, the best performance in Isik et al. (2016) was 10.8 dB with the help of a more complicated architecture.

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Authors and Affiliations

  1. Tencent AI Lab, Tencent, Bellevue, 98004, USA

    Yan-min Qian, Chao Weng & Dong Yu

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xuan-kai Chang & Shuai Wang

Authors
  1. Yan-min Qian
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  2. Chao Weng
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  3. Xuan-kai Chang
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  4. Shuai Wang
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  5. Dong Yu
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Correspondence to Yan-min Qian.

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Project supported by the Tencent and Shanghai Jiao Tong University Joint Project

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Qian, Ym., Weng, C., Chang, Xk. et al. Past review, current progress, and challenges ahead on the cocktail party problem. Frontiers Inf Technol Electronic Eng 19, 40–63 (2018). https://doi.org/10.1631/FITEE.1700814

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