1-D Local binary patterns based VAD used INHMM-based improved speech recognition

In this paper, 1-D Local binary patterns (LBP) are proposed to be used in speech signal segmentation and voice activation detection (VAD)and combined with hidden Markov model (HMM) for advanced speech recognition. Speech is firstly de-noised by Adaptive Empirical Model Decomposition (AEMD), and then processed using LBP based VAD. The short-time energy of the speech activity detected from the VAD is finally smoothed and used as the input of the HMM recognition process. The enhanced performance of the proposed system for speech recognition is compared with other VAD techniques at different SNRs ranging from 15 dB to a robust noisy condition at -5 dB.