An Improved Method for Automatic Classification of Speech

In this paper we present a novel method for silence/unvoiced/voiced (SUV) classification of speech signals. The well-known algorithm for locating endpoints in an utterance, based on zero-crossing rate and energy, was our starting point. We added a few supplementary decision criteria to it and we tested it using features like Teager energy and entropy. The experiments that we performed showed that these features performed better than the traditional energy measure for clean speech, but none of them produced a significant improvement in a noisy environment