基于Fisher线性判别分析的语音信号端点检测方法

传统的语音端点检测方法对辅音，特别是受到噪声污染的清音部分与背景噪声之间分离能力不足。针对上述问题，该文提出一种基于Fisher线性判别分析的梅尔频率倒谱系数(F-MFCC)端点检测方法。将清音信号和背景噪声视为两类分类问题，采用Fisher准则求解具有判别信息的最佳投影方向，使得投影后的特征参数具有最小类内散度和最大类间散度，从而增大清音与背景噪声的可分离性。在不同语音库上的实验结果表明，F-MFCC能够在不同信噪比和背景噪声条件下提高语音端点检测的准确率。

Voice Activity Detection Based on Fisher Linear Discriminant Analysis

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.