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Sparse Auditory Reproducing Kernel (SPARK) Features for Noise-Robust Speech Recognition

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2 Author(s)
Amin Fazel ; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, USA ; Shantanu Chakrabartty

In this paper, we present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as “Sparse Auditory Reproducing Kernel” (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a reproducing kernel Hilbert space (RKHS) spanned by overcomplete, nonlinear, and time-shifted gammatone basis functions. The feature extraction algorithm first involves computing kernel based similarity between the speech signal and the time-shifted gammatone functions, followed by feature pruning using a simple pooling technique (“MAX” operation). In this paper, we describe the effect of different hyper-parameters and kernel functions on the performance of a SPARK based speech recognizer. Experimental results based on the standard AURORA2 dataset demonstrate that the SPARK based speech recognizer delivers consistent improvements in word-accuracy when compared with a baseline speech recognizer trained using the standard ETSI STQ WI008 DSR features.

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:20 ,  Issue: 4 )
IEEE Biometrics Compendium
IEEE RFIC Virtual Journal
IEEE RFID Virtual Journal