Speech intelligibility and quality: A comparative study of speech enhancement algorithms

Abstract

Mobile devices are widely used today for speech communication. The environments in which these devices are used are widely varied and often the level of background noise in the speaker's environment can be significant. The purpose of speech enhancement is to reduce the level of background noise, ideally to such a level that it is not noticed by the listener. While speech enhancement algorithms can significantly reduce the noise level in a speech signal, improving speech quality, it is widely recognized that enhancement algorithms can have a negative impact on speech intelligibility. This paper compares the effect of three different speech enhancement algorithms on the intelligibility and the quality of speech. This work is the initial phase of an investigation into mitigating the impact of speech enhancement algorithms on speech intelligibility. The speech enhancement algorithms evaluated each use different approaches for noise reduction, namely, a statistical model-based algorithm, a noise estimation algorithm and a wavelet packet decomposition-based algorithm. Two objective speech intelligibility measurements and three objective speech quality measurements are used to assess the performance of the enhancement algorithms. The results of the experiments show that all the speech enhancement algorithms in this study have a negative impact on speech intelligibility to varying degrees.