首页|Performance analysis of voice activity detection algorithm for robust speech recognition system under different noisy environment
Performance analysis of voice activity detection algorithm for robust speech recognition system under different noisy environment
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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.
hidden markov model (HMM)subband OSF based voice activity detection (VAD)vector quantization
C Ganesh Babu、P T Vanathi、R Ramachandran、M Senthil Rajaa、R Vengatesh
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ECE, Bannari Amman Institute of Technology, Sathyamangalam, 638401, India
ECE, PSG College of Technology, Coimbatore, 641008, India
NETL
NSTL
