A statistical analysis on the impact of noise on MFCC features for speech recognition

U. Bhattacharjee, Swapnanil Gogoi, Rushali Sharma
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引用次数: 17

Abstract

Noise is omnipresent in almost all acoustical environments. The investigation presents here seeks to quantify the impact of noise on mel-frequency cepstral coefficients (MFCC) of speech signal. MFCC is one of the most commonly used features for speech recognition systems. However, it has been observed that performance of MFCC based system degrades drastically with changing noise levels and noise types. In the present study, different noise types at different levels have been added to the clean speech signal and the changes in statistical distribution pattern of the signal has been investigated. Further, performance of two commonly used noise normalization techniques Cepstral Mean and Variance Normalization (CMVN) and Spectral Subtraction (SS) have also been evaluated.
噪声对语音识别中MFCC特征影响的统计分析
在几乎所有的声学环境中,噪声是无所不在的。本文的研究旨在量化噪声对语音信号的梅尔频倒谱系数(MFCC)的影响。MFCC是语音识别系统中最常用的特征之一。然而,已经观察到基于MFCC的系统性能随着噪声水平和噪声类型的变化而急剧下降。在本研究中,我们在干净的语音信号中加入了不同程度的不同噪声类型,并研究了信号统计分布模式的变化。此外,还对两种常用的噪声归一化技术倒谱均值和方差归一化(CMVN)和谱减法(SS)的性能进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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