Speech/non-speech classification using multiple features for robust endpoint detection

Abstract

In this paper, we describe a new speech/non-speech classification method that improves the endpoint detection performance for speech recognition in noisy environments. The proposed method uses multiple features to increase the robustness in noisy environments, and the classification and regression tree (CART) technique is applied to effectively combine these multiple features for classification of each frame. We evaluate the performance of the proposed method by conducting speech/non-speech classification experiments on noisy speech. We also investigate the importance of various features on speech/non-speech classification in noisy environments In particular, the proposed method is applied to the endpoint detection algorithm for isolated speech recognition of a voice-dialing cellular phone. We simulate the speech recognition experiments in various noise environments, and the effects of the proposed method on speech recognition performance are evaluated.