On a Classification of Voiced/Unvoiced by using SNR for Speech Recognition

As communication medium of information, speech is not only used a lot, but also is the most comfortable. When we have conversation by speech, transmission of the information, which wanted to be delivered, is affected by the noise level. In speech signal processing, speech enhancement is using to improve speech signal corrupted by noise. Usually noise estimation algorithm need flexibility for variable environment and it can only apply on silence region to avoid effects of speech signal. So we have to preprocess finding voiced region before noise estimation. we proposed SNR estimation method for speech signal without silence region. For unvoiced speech signal, vocal track characteristic is reflected by noise, so we can estimate SNR by using spectral distance between spectrum of received signal and estimated vocal track. The proposed estimation method on voiced speech and the method by using voiced/unvoiced region energy are operated with simple logic as time domain method. And the estimation method on unvoiced region is possible to estimated noise level for narrow-band speech signal by using vocal track properties. It can be applied to rate decision of vocoder and used for pre-processing to decide threshold of noise reduction. It is often necessary to perform speech enhancement through noise removal in speech processing systems operating in noisy environments. As the presence of noise degrades the performance of speech coders and voice recognition system 10,11 . It is therefore common to incorporate speech enhancement as a preprocessing step in these systems. The other important application of speech enhancement is to improve the perceptual quality of speech in order to reduce listener's fatigue. The additive noise may be due to the noisy environment in which the speaker is speaking, or it may arise from noise in the transmission media. Furthermore, most of these algorithms only attempt to modify the spectral amplitudes of the noise corrupted speech signal in order to reduce the effect of the noise component while leaving the noise corrupted phase information intact. we study the performance of these filters for the enhancement of speech contaminated by additive white noise. Performance comparisons are accomplished in terms of