Title

Robust Speaker Recognition System Employing Covariance Matrix And Eigenvoice

Keywords

2D-FFT; 2D-PCA; Covariance matrix; Eigenvectors; Hamming window

Abstract

This paper presents an original speaker recognition system that utilizes a quantized spectral covariance matrix on the input to a two-dimensional Principal Component Analysis (2DPCA) function. Eigenvoice algorithm is used as a classifying tool and is generated by the features of a group of speakers. The proposed system is selective in acquiring acoustic parameters and leads to a significant decrease in storage requirements. The system is robust in a noisy environment with recognition rates as high as 92% at 0dB SNR. Concatenated vowels that make up the speech signal are extracted from the TIMIT database and the noise environment is acquired from the NOIZEOUS database. © 2013 IEEE.

Publication Date

12-1-2013

Publication Title

Midwest Symposium on Circuits and Systems

Number of Pages

1116-1119

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/MWSCAS.2013.6674848

Socpus ID

84893159382 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893159382

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