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A NOVEL APPROACH TO SOFT-MASK ESTIMATION AND LOG-SPECTRAL ENHANCEMENT FOR ROBUST SPEECH RECOGNITION
 

Summary: A NOVEL APPROACH TO SOFT-MASK ESTIMATION AND LOG-SPECTRAL
ENHANCEMENT FOR ROBUST SPEECH RECOGNITION
Julien van Hout, Abeer Alwan
Electrical Engineering Department, University of California, Los Angeles.
julienvanhout@ucla.edu, alwan@ee.ucla.edu
ABSTRACT
This paper describes a technique for enhancing the Mel-filtered log
spectra of noisy speech, with application to noise robust speech
recognition. We first compute an SNR-based soft-decision mask in
the Mel-spectral domain as an indicator of speech presence. Then,
we exploit the known time-frequency correlation of speech by
treating this mask as an image, and performing median filtering
and blurring to remove the outliers and to smooth the decision
regions. This mask constitutes a set of multiplicative coefficients
(ranging in [0,1]) that are used to discard the unreliable parts of the
Mel-filtered log-spectrum of noisy speech. Finally, we apply Log-
Spectral Flooring [1] on the liftered spectra of both clean and noisy
speech so as to match their respective dynamic ranges and to em-
phasize the information in the spectral peaks. The noisy MFCCs
computed on these modified log-spectra show an increased similar-

  

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

 

Collections: Computer Technologies and Information Sciences