基于Fisher線性判別分析的語音信號端點檢測方法

王明合; 張二華; 唐振民; 許昊

doi:10.11999/JEIT141122

基于Fisher線性判別分析的語音信號端點檢測方法

doi: 10.11999/JEIT141122

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出版歷程
- 收稿日期: 2014-08-29
- 修回日期: 2014-12-19
- 刊出日期: 2015-06-19

Voice Activity Detection Based on Fisher Linear Discriminant Analysis

摘要

摘要: 傳統(tǒng)的語音端點檢測方法對輔音，特別是受到噪聲污染的清音部分與背景噪聲之間分離能力不足。針對上述問題，該文提出一種基于Fisher線性判別分析的梅爾頻率倒譜系數(shù)(F-MFCC)端點檢測方法。將清音信號和背景噪聲視為兩類分類問題，采用Fisher準(zhǔn)則求解具有判別信息的最佳投影方向，使得投影后的特征參數(shù)具有最小類內(nèi)散度和最大類間散度，從而增大清音與背景噪聲的可分離性。在不同語音庫上的實驗結(jié)果表明，F(xiàn)-MFCC能夠在不同信噪比和背景噪聲條件下提高語音端點檢測的準(zhǔn)確率。
- 語音處理 /
- 語音端點檢測 /
- 梅爾頻率倒譜系數(shù) /
- Fisher線性判別分析
Abstract: Traditional Voice Activity Detection (VAD) approaches can not effectively detect consonant as well as noisy unvoiced consonant. To address this problem, this paper proposes a VAD approach Mel Frequency Cepstrum Coefficient (F-MFCC) based on Fisher linear discriminant analysis, in consideration of two-class issue regarding to consonant and background noise. Fisher criterion rule is used to solve the optimal projection vector, building upon which we can minimize the within-class scatter can be minimized and the between-class scatter can be maximized, as a result to enhance separability between consonant and background noise. Extensive experiments are conducted to evaluate the F-MFCC performance. The results demonstrate that, under different SNR and noise conditions, the proposed approach achieves higher VAD accuracy.
- Speech processing /
- Voice Activity Detection (VAD) /
- Mel Frequency Cepstrum Coefficient (MFCC) /
- Fisher linear discriminant analysis

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