Abstract—The paradigm multi-stream has been shown to
result in features combined that can help to increase the
robustness of distributed speech recognition (DSR) in the mobile
communications. In this paper, we employs a combination of
post proceeded Mel-cepstral coefficients (MFCCs) and line
spectral frequencies features (LSFs) projected in linear
discriminate analysis (LDA) space. The experiments performed
on the Aurora 2.0 database using multi-condition training set
show that, even with fewer parameters, the proposed front-end
provides comparable recognition results to the standard ETSI
WI008 advanced front-end, nowadays available in the vocal
commands of the GSM mobile communications, while achieving
higher accuracy when the signal-to-noise ratio (SNR) is very
low.
Index Terms—Distributed speech recognition, linear
discriminate analysis, multi-stream hidden Markov models,
Mel-cepstral coefficients, line spectral frequencies.
M. R. L. Daalache is with the University of Technology USTHB, P.O.
Box 32 Bab Ezzouar, Algiers, Algeria (Corresponding author, email:
mdaalache@usthb.dz).
D. Addou is with the University of Technology USTHB, P.O. Box 32 Bab
Ezzouar, Algiers, Algeria (email: daddou@usthb.dz).
M. Boudraa is with Faculty of Electronics and Computer Sciences, FEI,
Bab Ezzouar, Algiers, Algeria (email: mboudraa@usthb.dz).
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Cite:M. R. L. Daalache, D. Addou, and M. Boudraa, "A Front-End Processing Using Subspace-Based Speech Enhancement over Mobile Devices," Journal of Advances in Computer Networks vol. 2, no. 1, pp. 63-66, 2014.
