Performance analysis of voice activity detection algorithm for robust speech recognition system under different noisy environment

515-522This study evaluates performance of objective measures in terms of predicting quality of noisy input speech signal usingvoice activity detection (VAD). Implementation process includes a speech-to-text system using isolated word recognition with avocabulary of 10 words (digits 0-9) and statistical modeling (Hidden Markov Model - HMM) for machine speech recognition. Intraining period, uttered digits were recorded using 8-bit pulse code modulation (PCM) with a sampling rate of 8 KHz and save asa wave format file using sound recorder software. HMM performs speech analysis using linear predictive coding (LPC) methodof degree. For a given word in vocabulary, system builds an HMM model and trains model during training phase. Training stepsfrom VAD to HMM model building are performed using PC-based Matlab programs. Current framework uses automatic speechrecognition (ASR) with HMM based classification and noise language modeling to achieve effective noise knowledge estimation