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)
An acoustic-phonetic forensic-voice-comparison system extracted information from the formant trajectories of tokens of Standard Chinese /iau/. When this information was added to a generic automatic forensic-voice-comparison system, which did not itself exploit acoustic-phonetic information, there was a substantial improvement in system validity but a(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)
Defining the relevant population to sample is an important issue in data-based implementation of the likelihood-ratio framework for forensic voice comparison. We present a logical argument that because an investigator or prosecutor only submits suspect and offender recordings for forensic analysis if they sound sufficiently similar to each other, the(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 (Cllr) 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)
Group delay is proposed as an effective means of representing spectral phase information as a feature in speaker recognition. Robustness of group delay features is difficult to achieve, since the spiky nature of the group delay masks the fine structure of the group delay. In this paper, two features based on group delay are proposed by reducing the effect(More)
Frequency modulation (FM) features are typically extracted using a filterbank, usually based on an auditory frequency scale, however there is psychophysical evidence to suggest that this scale may not be optimal for extracting speakerspecific information. In this paper, speaker-specific information in FM features is analyzed as a function of the filterbank(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)
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)