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.