Gaussian model-based dynamic time warping system and method for speech processing

摘要

The Gaussian Dynamic Time Warping model provides a hierarchical statistical model for representing an acoustic pattern. The first layer of the model represents the general acoustic space; the second layer represents each speaker space and the third layer represents the temporal structure information contained in each enrollment speech utterance, based on equally-spaced time intervals. These three layers are hierarchically developed: the second layer is derived from the first, and the third layer is derived from the second. The model is useful in speech processing application, particularly in applications such as word and speaker recognition, using a spotting recognition mode.