Performance analysis of voice activity detection algorithm for robust speech recognition system under different noisy environment
Babu, C Ganesh
Vanathi, P T
Rajaa, M Senthil
This study evaluates performance of objective measures in terms of predicting quality of noisy input speech signal using voice activity detection (VAD). Implementation process includes a speech-to-text system using isolated word recognition with a vocabulary of 10 words (digits 0-9) and statistical modeling (Hidden Markov Model – HMM) for machine speech recognition. In training period, uttered digits were recorded using 8-bit pulse code modulation (PCM) with a sampling rate of 8 KHz and save as a wave format file using sound recorder software. HMM performs speech analysis using linear predictive coding (LPC) method of degree. For a given word in vocabulary, system builds an HMM model and trains model during training phase. Training steps from VAD to HMM model building are performed using PC-based Matlab programs. Current framework uses automatic speech recognition (ASR) with HMM based classification and noise language modeling to achieve effective noise knowledge estimation.