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Sparse coding of the modulation spectrum for noise-robust automatic speech recognition

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Research areas:
Year:
2014
Type of Publication:
Misc
Authors:
Ahmadi, Sara; Cranen, Bert; Boves, Lou
How published:
Poster presented at the INSPIRE Winter School on Concepts and Computational Models of Robust Bottom-Up Encoding, Copenhagen, Denmark
Month:
January
Abstract:
The full modulation spectrum is a high-dimensional representation of a one-dimensional audio signal. A conventional band-pass filter yields as many parallel output signals as there are bands (15 in the system used in this paper). We use the Hilbert transform to obtain the envelope of these output signals, which are then low-pass filtered and downsampled. Each downsampled signal is converted to a number of parallel signals by a modulation band-pass filter (which contained 9 filters in our implementation). The resulting 15×9 = 135 dimensional signal yields a very rich representation of the original signal. It is reason-able to assume that modulation components related to speech signals, which are concentrated in modula-tion frequencies between 4 Hz and 16 Hz, occupy different modulation bands than additive noise. Therefore, the modulation spectrum is a promising representation for noise-robust ASR if we can develop a method for using samples of the modulation spectrum for estimating the probability of the states in a finite state representation of speech signals. In this paper we use Sparse Coding to obtain such probabilities. A straightforward implementation yields promising results on the AURORA-2 task. We analyze the performance in detail, and propose several options for future developments that may improve the performance beyond the state of the art.
Full text: P1-SaraAhmadi.pdf
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