An overview of speech endpoint detection algorithms

被引：22

作者：

Zhang, Tao ^{[1
]}

Shao, Yangyang ^{[1
]}

Wu, Yaqin ^{[1
]}

Geng, Yanzhang ^{[1
]}

Fan, Long ^{[1
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

来源：

APPLIED ACOUSTICS | 2020年 / 160卷

关键词：

Speech endpoint detection; Time domain; Frequency domain; Cepstrum domain; Neural network; VOICE ACTIVITY DETECTION; SUPPORT VECTOR MACHINE; ENERGY NORMALIZATION; ROBUST; RECOGNITION; SPECTRUM;

D O I：

10.1016/j.apacoust.2019.107133

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Speech endpoint detection is an important part of modern speech information processing technology. The success of endpoint detection directly improves the performance and quality of speech coding, speech recognition, speech synthesis and human interaction. The robustness and detection accuracy of algorithms have always been hot topics for many scholars in the condition of low Signal-to-Noise Ratio (SNR) and complex noise. In this paper, we aim to provide an overview of the state-of-the-art in time domain, frequency domain and cepstrum domain for speech endpoint detection algorithms and to cast a glance at the challenges for future research. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：16

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