A Statistical Approach to Mel-Domain Mask Estimation for Missing-Feature ASR

In this letter, we present a statistical approach to Mel-domain mask estimation for missing feature (MF)-based automatic speech recognition (ASR). Mel-domain time-frequency masks are of interest, since MF systems have been shown successful in that domain. Time- and channel-specific reliability measures are derived as posterior probabilities of active speech using a 2-state speech model. Since closed form distributions for Mel-domain spectra do not exist, they are instead modeled as χ² processes with empirically-determined degrees of freedom. Additionally, we present HMM-based decoding to exploit temporal correlation of spectral speech data. The proposed mask estimation algorithm is integrated with an example MF-based ASR front-end from, and is shown to outperform the spectral subtraction (SS)-based method from in terms of word-accuracy, when applied to the Aurora-2 database.