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基于子帶雙特征的自適應(yīng)保留似然比魯棒語音檢測(cè)算法

何偉俊,  賀前華,  吳俊峰,  楊繼臣

何偉俊, 賀前華, 吳俊峰, 楊繼臣. 基于子帶雙特征的自適應(yīng)保留似然比魯棒語音檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(11): 2879-2886. doi: 10.11999/JEIT160157
引用本文: 何偉俊, 賀前華, 吳俊峰, 楊繼臣. 基于子帶雙特征的自適應(yīng)保留似然比魯棒語音檢測(cè)算法[J]. 電子與信息學(xué)報(bào), 2016, 38(11): 2879-2886. doi: 10.11999/JEIT160157
HE Weijun, HE Qianhua, WU Junfeng, YANG Jichen. Adaptively Reserved Likelihood Ratio-based Robust Voice Activity Detection with Sub-band Double Features[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2879-2886. doi: 10.11999/JEIT160157
Citation: HE Weijun, HE Qianhua, WU Junfeng, YANG Jichen. Adaptively Reserved Likelihood Ratio-based Robust Voice Activity Detection with Sub-band Double Features[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2879-2886. doi: 10.11999/JEIT160157

基于子帶雙特征的自適應(yīng)保留似然比魯棒語音檢測(cè)算法

doi: 10.11999/JEIT160157
基金項(xiàng)目: 

國(guó)家自然科學(xué)基金 (61571192)，廣東省公益項(xiàng)目(2015A010103003)，中央高?；究蒲袠I(yè)務(wù)費(fèi)項(xiàng)目華南理工大學(xué)(2015ZM143)

Adaptively Reserved Likelihood Ratio-based Robust Voice Activity Detection with Sub-band Double Features

Funds: 

The National Natural Science Foundation of China (61571192), The Science and Technology Foundation of Guangdong Province (2015A010103003), The Fundamental Research Funds for the Central Universities, SCUT (2015ZM143)

  • 摘要: 為了進(jìn)一步提高低信噪比下語音激活檢測(cè)(VAD)的準(zhǔn)確率，該文提出一種基于子帶雙特征的自適應(yīng)保留似然比魯棒語音激活檢測(cè)算法。算法采用子帶歸一化最大自相關(guān)函數(shù)與子帶歸一化平均過零率雙重特征設(shè)置頻率分量似然比的保留權(quán)值，同時(shí)利用已過去固定時(shí)長(zhǎng)的VAD判決結(jié)果及對(duì)應(yīng)的子帶特征參數(shù)自適應(yīng)地估計(jì)似然比的保留閾值。實(shí)驗(yàn)結(jié)果表明，此算法的VAD檢測(cè)準(zhǔn)確率相比原保留似然比算法在10 dB, 0 dB和-10 dB平穩(wěn)白噪聲下分別提高了1.2%, 7.2%和8.1%，在10 dB和0 dB非平穩(wěn)Babble噪聲下分別提高了1.6%和3.4%。當(dāng)其被用于2.4 kbps低速率聲碼器系統(tǒng)時(shí)，合成語音的感知語音質(zhì)量評(píng)價(jià)(PESQ)比原聲碼器系統(tǒng)在白噪聲下提高了0.098~0.153，在Babble噪聲下提高了0.157~0.186。
    Abstract: In order to improve the correct rate of Voice Activity Detection (VAD) in low Signal Noise Ratio (SNR) environment, the paper presents an adaptive reserved likelihood ratio VAD method, which is based on sub-band double features. The method employs sub-band auto correlate function and sub-band zero crossing rate in the process of setting reserved weight. Reserved threshold is estimated adaptively according to the passed VAD results and their sub-band feature parameters. The experiment shows its promising performance in comparison with similar algorithms, the VAD correct rate is improved by 1.2%, 7.2%, and 8.1% respectively in 10 dB, 0 dB, and -10 dB stationary white noisy environment, 1.6% and 3.4% respectively in 10 dB and 0 dB non-stationary Babble noisy environment. The method is also applied to 2.4 kbps low bit rate vocoder and the Perceptual Evaluation of Speech Quality (PESQ) is improved by 0.098~0.153 in white noisy environment, 0.157~0.186 in Babble noisy environment.
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出版歷程
  • 收稿日期:  2016-02-04
  • 修回日期:  2016-06-27
  • 刊出日期:  2016-11-19

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