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Vector Taylor Series Expansion with Auditory Masking for Noise Robust Speech Recognition

Citation Author(s):
Ashish Panda
Submitted by:
Biswajit Das
Last updated:
14 October 2016 - 8:15am
Document Type:
Presentation Slides
Document Year:
2016
Event:
Presenters:
Biswajit Das
Paper Code:
ISCSLP1601

Abstract

In this paper, we address the problem of speech recognition in
the presence of additive noise. We investigate the applicability
and efficacy of auditory masking in devising a robust front end
for noisy features. This is achieved by introducing a masking
factor into the Vector Taylor Series (VTS) equations. The resultant
first order VTS approximation is used to compensate the parameters
of a clean speech model and a Minimum Mean Square
Error (MMSE) estimate is used to estimate the clean speech
features. The proposed algorithms are validated through experiments
on a noise corrupted TIMIT speech recognition database.
We show significant performance gain for the proposed method
as compared to the traditional VTS algorithm.

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