Empty Speech Pause Detection Algorithms’ Comparison

Nowadays, empty speech pause detection algorithms play an important role in several speech processing fields such as speech recognition, speech enhancement, and speech coding. This work describes two new pause detection algorithms and compare their performance with four standard Voice Activity Detection (VAD) methods represented by the adaptive Long Term Spectral Divergence (LTSD) algorithm, the Likelihood Ratio Test (LRT) algorithm, the Neural Network thresholding and G.729. The proposed algorithms exploit the concept of adaptation in order to handle adverse conditions. The test data are recordings of spontaneous speech made in noisy environments. The experimental results show that the performance of proposed algorithms on noisy and even artificially cleaned speech are superior than that achieved by standard methods reported in literature.