USP Electronic Research Repository

A Novel Algorithm of Sparse Representations for Speech Compression/Enhancement and Its Application in Speaker Recognition System

Singh, Satyanand and Assaf, Mansour and Kumar, Abhay (2016) A Novel Algorithm of Sparse Representations for Speech Compression/Enhancement and Its Application in Speaker Recognition System. International Journal of Computational and Applied Mathematics, 11 (1). pp. 89-104. ISSN 1819-4966

Full text not available from this repository.

Abstract

This paper proposes sparse and redundancy representation spectral domain compression of the speech signal using novel sparsing algorithms to the problem of speech compression (SC)/enhancement (SE). In Automatic Speaker Recognition (ASR) sparsification can play a major role to resolve big data issues in speech compression and its storage in the database, where the speech signal can be uncompressed before applying to ASR system. The speech signal is converted to a spectral domain using Discrete Rajan Transform (DRT) and only first and mid spectrum component is retained forcing the remaining component to zero. The speech signal spectrum can be maximally compressed 8:1 ratio to the unique one. Spectrally compressed speech signal can be stored in the database and during training and testing time it can be synthesized using Inverse Discrete Rajan Transform (IDRT) in ASR. Sparsification and spectral compression up to 75% with Equal Error Rate (EER) of ASR is 3%. Percentage of Identification Accuracy (PIA) of ASR with sparsification and speech enhancement is 99.1% and without sparsification 98.8% for TIMIT database respectively.

Item Type: Journal Article
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User: Mansour Assaf
Date Deposited: 19 Sep 2016 14:35
Last Modified: 19 Sep 2016 14:35
URI: http://repository.usp.ac.fj/id/eprint/9243
UNSPECIFIED

Actions (login required)

View Item