Robust Endpoint Detection for Speech Recognition Based on Discriminative Feature Extraction

Accurate endpoint detection is important for improving the speech recognition capability. This paper proposes a novel endpoint detection method which combines energy-based and likelihood ratio-based voice activity detection (VAD) criteria, where the likelihood ratio is calculated with speech/non-speech Gaussian mixture models (GMMs). Moreover, the proposed method introduces the discriminative feature extraction technique (DFE) in order to improve the speech/non-speech classification. The DFE is used in the training of parameters required for calculating the likelihood ratio. Experimental results have shown that the proposed endpointer achieves good performance compared to an energy-based endpointer in terms of start-of-speech (SOS) and end-of-speech (EOS) detections. Due to the improvement of the endpointer, the performance of automatic speech recognition (ASR) has also been improved.