Deep-Sparse-Representation-Based Features for Speech Recognition

TLDR: This paper proposes to use a multilevel decomposition (having multiple layers), also known as the deep sparse representation (DSR), to derive a feature representation for speech recognition, and reveals that the representations obtained at different sparse layers of the proposed DSR model have complimentary information.

Abstract: Features derived using sparse representation (SR)-based approaches have been shown to yield promising results for speech recognition tasks. In most of the approaches, the SR corresponding to speech signal is estimated using a dictionary, which could be either exemplar based or learned. However, a single-level decomposition may not be suitable for the speech signal, as it contains complex hierarchical information about various hidden attributes. In this paper, we propose to use a multilevel decomposition (having multiple layers), also known as the deep sparse representation (DSR), to derive a feature representation for speech recognition. Instead of having a series of sparse layers, the proposed framework employs a dense layer between two sparse layers, which helps in efficient implementation. Our studies reveal that the representations obtained at different sparse layers of the proposed DSR model have complimentary information. Thus, the final feature representation is derived after concatenating the representations obtained at the sparse layers. This results in a more discriminative representation, and improves the speech recognition performance. Since the concatenation results in a high-dimensional feature, principal component analysis is used to reduce the dimension of the obtained feature. Experimental studies demonstrate that the proposed feature outperforms existing features for various speech recognition tasks.