CLC number: TP391.4
On-line Access: 2024-08-27
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Yan-min Qian, Chao Weng, Xuan-kai Chang, Shuai Wang, Dong Yu. Past review, current progress, and challenges ahead on the cocktail party problem[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 40-63.
@article{title="Past review, current progress, and challenges ahead on the cocktail party problem",
author="Yan-min Qian, Chao Weng, Xuan-kai Chang, Shuai Wang, Dong Yu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="19",
number="1",
pages="40-63",
year="2018",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700814"
}
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%T Past review, current progress, and challenges ahead on the cocktail party problem
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%A Xuan-kai Chang
%A Shuai Wang
%A Dong Yu
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700814
TY - JOUR
T1 - Past review, current progress, and challenges ahead on the cocktail party problem
A1 - Yan-min Qian
A1 - Chao Weng
A1 - Xuan-kai Chang
A1 - Shuai Wang
A1 - Dong Yu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
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SP - 40
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%@ 2095-9184
Y1 - 2018
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700814
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.
This article has been corrected, see doi:10.1631/FITEE.19e0001
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