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Evaluation of a speaker identification system with and without fusion using three databases in the presence of noise and handset effects

Lookup NU author(s): Musab Al-Kaltakchi, Dr Wai Lok Woo, Professor Satnam Dlay, Professor Jonathon Chambers

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2017, The Author(s). In this study, a speaker identification system is considered consisting of a feature extraction stage which utilizes both power normalized cepstral coefficients (PNCCs) and Mel frequency cepstral coefficients (MFCC). Normalization is applied by employing cepstral mean and variance normalization (CMVN) and feature warping (FW), together with acoustic modeling using a Gaussian mixture model-universal background model (GMM-UBM). The main contributions are comprehensive evaluations of the effect of both additive white Gaussian noise (AWGN) and non-stationary noise (NSN) (with and without a G.712 type handset) upon identification performance. In particular, three NSN types with varying signal to noise ratios (SNRs) were tested corresponding to street traffic, a bus interior, and a crowded talking environment. The performance evaluation also considered the effect of late fusion techniques based on score fusion, namely, mean, maximum, and linear weighted sum fusion. The databases employed were TIMIT, SITW, and NIST 2008; and 120 speakers were selected from each database to yield 3600 speech utterances. As recommendations from the study, mean fusion is found to yield overall best performance in terms of speaker identification accuracy (SIA) with noisy speech, whereas linear weighted sum fusion is overall best for original database recordings.


Publication metadata

Author(s): Al-Kaltakchi MTS, Woo WL, Dlay S, Chambers JA

Publication type: Article

Publication status: Published

Journal: EURASIP Journal on Advances in Signal Processing

Year: 2017

Volume: 2017

Online publication date: 02/12/2017

Acceptance date: 13/11/2017

Date deposited: 17/01/2018

ISSN (print): 1687-6172

ISSN (electronic): 1687-6180

Publisher: Springer International Publishing

URL: https://doi.org/10.1186/s13634-017-0515-7

DOI: 10.1186/s13634-017-0515-7


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