Model based speech pause detection

This paper presents two new algorithms for robust speech pause detection (SPD) in noise. Our approach was to formulate SPD into a statistical decision theory problem for the optimal detection of noise-only segments, using the framework of model-based speech enhancement (MBSE). The advantages of this approach are that it performs well in high noise conditions, all necessary information is available in MBSE, and no other features are required to be computed. The first algorithm is based on a maximum a posteriori probability (MAP) test and the second is based on a Neyman-Pearson test. These tests are seen to make use of the spectral distance between the input vector and the composite spectral prototypes of the speech and noise models, as well as the probabilistic framework of the hidden Markov model. The algorithms are evaluated and shown to perform well against different types of noise at various SNRs.