NDLI: Application of automatic speaker verification techniques for forensic evidence evaluation

Content Provider	IEEE Xplore Digital Library
Author	Adikari, A.M.T.S.B. Devadithya, S. Bandara, A.R.S.T. Dharmawardane, K.C.J. Wavegedara, K.C.B.
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Telecommun. Eng., Univ. of Moratuwa, Moratuwa, Sri Lanka (Adikari, A.M.T.S.B.; Devadithya, S.; Bandara, A.R.S.T.; Dharmawardane, K.C.J.; Wavegedara, K.C.B.)
Abstract	When applying speaker verification techniques for forensic evidence evaluation, many challenges arise. One such challenge is the interpretation of the verification results in terms of probability. The output of the existing speaker verification is a ratio between two likelihood values and hence does not have a very meaningful representation. In this paper, we introduce a method which outputs a posterior probability function, such that given the evidence, the probability it came from a certain suspect can be calculated. Another key challenge in forensic evidence evaluation is the practical difficulty in recreating the evidence environment in order to obtain the training voice samples from the suspect. For this we propose a novel approach based on the Baysean probability framework, which uses a pre-computed Universal Background Model (UBM). The speaker verification system is built around Mel Frequency Cepstral Coefficients (MFCC) for feature extraction and Gaussian Mixture Models (GMM) for speaker modeling. The optimum values for different parameters of these techniques are found experimentally. Our results demonstrate that a model with 32 mixtures and a dimension of size 13 gives the best performance, as a trade-off between the accuracy and the computational efficiency.
Starting Page	444
Ending Page	448
File Size	307919
Page Count	5
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900703
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Mel frequency cepstral coefficient Forensics Feature extraction Training Digital signal processing Computational modeling UBM speaker verification forensics MFCC GMM
Content Type	Text
Resource Type	Article

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