Tharmarajah Thiruvaran

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I4U is a joint entry of nine research Institutes and Universities across 4 continents to NIST SRE 2012. It started with a brief discussion during the Odyssey 2012 workshop in Singapore. An online discussion group was soon set up, providing a discussion platform for different issues surrounding NIST SRE'12. Noisy test segments, uneven multi-session training,(More)
This paper describes the performance of the I4U speaker recognition system in the NIST 2008 Speaker Recognition Evaluation. The system consists of seven subsystems, each with different cepstral features and classifiers. We describe the I4U Primary system and report on its core test results as they were submitted, which were among the best-performing(More)
Most conventional features used in speaker recognition are based on spectral envelope characterizations such as Mel-scale filterbank cepstrum coefficients (MFCC), Linear Prediction Cepstrum Coefficient (LPCC) and Perceptual Linear Prediction (PLP). The MFCC's success has seen it become a de facto standard feature for speaker recognition. Alternative(More)
Defining the relevant population to sample is an important issue in data-based implementation of the likelihood-ratio framework for forensic voice comparison. A forensic likelihood ratio is the answer to a specific question which depends on the prosecution and defence hypotheses and the circumstances of the case. If an inappropriate background sample is(More)
The issues of validity and reliability are important in forensic science. Within the likelihood-ratio framework for the evaluation of forensic evidence, the log-likelihood-ratio cost (C llr) has been applied as an appropriate metric for evaluating the accuracy of the output of a forensic-voice-comparison system, but there has been little research on(More)
Frequency modulation (FM) information from the speech signal is herein proposed to complement the conventional amplitude based features for automatic forensic speaker recognition systems. In addition to presenting the AM-FM model of speech used to generate the proposed frequency modulation features, the significance of frequency modulation for speaker(More)
In this paper, the fusion of two speaker recognition subsystems, one based on Frequency Modulation (FM) and another on MFCC features, is reported. The motivation for their fusion was to improve the recognition accuracy across different types of channel variations, since the two features are believed to contain complementary information. It was found that(More)