韧性语音识别技术

在实际环境中,背景噪声是广泛存在的,而且严重损害诸如语音理解与识别技术应用的效果。本文主要讨论噪声分布是近正态分布的情况。噪声的概率密度函数(p、d、f)为如下形式:f(x)=(1-ε)g(x)+εg(x)其中,g(x)是正态概率密度函数,h(x)是任意对称的概率密度函数,ε是一个常数,0<ε<1。借助于Robust的基本思想,我们对线性预测的残差进行了韧性处理,得到Robust M估计。减少了噪声中语言参数的估计偏差,而且导出了Robust M估计的格点算法并给出了实验数据和结果。

ROBUST SPEECH RECOGNITION TECHNIQUE

Background noise acoustically adding to speech can degrade the performance of digital environments for applications such as speech comperession and recognition. In this paper we are primarily interested in those situation for which it is known that the noise distribution is nearly normal.We take the noise probability density function (p. d. f) to be the form.As f(x)=(1-ε)g(x)+εh(x) where g(x) is the normal p.d.f,A(x) is an arbitrary symmetric p.d.f,and ε is constant where 0<ε<1. By means of robust estimation,we give the construction of robust M-estimation adding effective function in linear Predictive residue. In ordar to reduce the deviation of estimated parameters of noisy speech, we present a procedure for estimating the robust M and give the experiment data and results.