Robust endpoint detection and energy normalization for real-time speech and speaker recognition

When automatic speech recognition (ASR) and speaker verification (SV) are applied in adverse acoustic environments, endpoint detection and energy normalization can be crucial to the functioning of both systems. In low signal-to-noise ratio (SNR) and nonstationary environments, conventional approaches to endpoint detection and energy normalization often fail and ASR performances usually degrade dramatically. The purpose of this paper is to address the endpoint problem. For ASR, we propose a real-time approach. It uses an optimal filter plus a three-state transition diagram for endpoint detection. The filter is designed utilizing several criteria to ensure accuracy and robustness. It has almost invariant response at various background noise levels. The detected endpoints are then applied to energy normalization sequentially. Evaluation results show that the proposed algorithm significantly reduces the string error rates in low SNR situations. The reduction rates even exceed 50% in severa...

• Type: Journal
• Year: 2002
• Where: TASLP
• Authors: Qi Li, Jinsong Zheng, A. Tsai, Qiru Zhou
• Keywords: Endpoint Detection, Energy Normalization, Optimal Filter, TASLP 2002