FEATURE COMPENSATION APPROACH TO ROBUST SPEECH RECOGNITION

摘要

Described is a technology by which a feature compensation approach to speech recognition uses a high-order vector Taylor series (HOVTS) approximation of a model of distortions to improve recognition accuracy. Speech recognizer models trained with clean speech degrade when later dealing with speech that is corrupted by additive noises and convolutional distortions. The approach attempts to remove any such noise/distortions from the input speech. To use the HOVTS approximation, a Gaussian mixture model is trained and used to convert cepstral domain feature vectors to log spectrum components. HOVTS computes statistics for the components, which are transformed back to the cepstral domain. A noise/distortion estimate is obtained, and used to provide a clean speech estimate to the recognizer.