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

Summary: 1
A Statistical Approach to Mel-Domain Mask
Estimation for Missing-Feature ASR
Bengt J. Borgstršom, Student Member, IEEE, and Abeer Alwan, IEEE Fellow
Abstract-- 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
2
processes with empirically-determined degrees of freedom.
Additionally, we present HMM-based decoding to exploit tem-
poral correlation of spectral speech data. The proposed mask
estimation algorithm is integrated with an example MF-based
ASR front-end from [14], and is shown to outperform the spectral
subtraction (SS)-based method from [10] in terms of word-
accuracy, when applied to the Aurora-2 database.

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences