NOISE-ROBUST SPEECH RECOGNITION WITH EXEMPLAR-BASED SPARSE REPRESENTATIONS USING ALPHA-BETA DIVERGENCE

In this paper, we investigate the performance of a noise-robust sparse representations (SR)-based recognizer using the Alpha-Beta (AB)divergence to compare the noisy speech segments and exemplars. The baseline recognizer, which approximates noisy speech segments as a linear combination of speech and noise exemplars of variable length, uses the generalized Kullback-Leibler divergence to quantify the approximation quality. Incorporating a reconstruction errorbased back-end, the recognition performance highly depends on the congruence of the divergence measure and used speech features. Having two tuning parameters, namely alpha and beta, the AB-divergence provides improved robustness against background noise and outliers. These parameters can be adjusted for better performance depending on the distribution of speech and noise exemplars in the high-dimensional feature space. Moreover, various well-known distance/divergence measures such as the Euclidean distance, generalized Kullback-Leibler divergence, Itakura-Saito divergence and Hellinger distance are special cases of the AB-divergence for different (alpha, beta) values. The goal of this work is to investigate the optimal divergence for mel-scaled magnitude spectral features by performing recognition experiments at several SNR levels using different (alpha, beta) pairs. The results demonstrate the effectiveness of the AB-divergence compared to the generalized Kullback-Leibler divergence especially at the lower SNR levels.