An Extension of STFT Uncertainty Propagation for GMM-Based Super-Gaussian a Priori Models

Abstract

Feature compensation is a low computational cost technique to achieve robust automatic speech recognition (ASR). Short-time Fourier Transform Uncertainty Propagation (STFT-UP) provides feature compensation in domains used for ASR as, e.g., Mel-Frequency Cepstra Coefficient (MFCC), while using STFT domain distortion models. However, STFT-UP is limited to Gaussian priors when modeling speech distortion, whereas super-Gaussian priors are known to provide improved performance. In this letter, an extension of STFT-UP is presented that uses approximate super-Gaussian priors. This is achieved by extending the conventional complex Gaussian priors to complex Gaussian mixture priors. The approach can be applied to any of the STFT-UP existing solutions, thus providing super-Gaussian uncertainty propagation. The method is exemplified by a Minimum Mean Square Error (MMSE) MFCC estimator with an approximate generalized Gamma speech prior. This estimator clearly outperforms the Gaussian-based MMSE-MFCC feature compensation on the AURORA4 corpus.