Miranti Indar Mandasari

Quality Measure Functions for Calibration of Speaker Recognition Systems in Various Duration Conditions

This paper investigates the effect of utterance duration to the calibration of a modern i-vector speaker recognition system with probabilistic linear discriminant analysis (PLDA) modeling. A calibration approach to deal with these effects using quality measure functions (QMFs) is proposed to include duration in the calibration transformation. Extensive experiments are performed in order to evaluate the robustness of the proposed calibration approach for unseen conditions in the training of calibration parameters. Using the latest NIST corpora for evaluation, results highlight the importance of considering the quality metrics like duration in calibrating the scores for automatic speaker recognition systems.

The effect of noise on modern automatic speaker recognition systems

Motivated by the application of speaker recognition in forensic area, this paper presents a study on noise robustness of several automatic speaker recognition system approaches, ranging from simple dot-scoring and a standard i-vector system with cosine distance scoring to a state-of-the-art i-vector Probabilistic Linear Discriminant Analysis (PLDA) system. Using the recent NIST 2010 Speaker Recognition Evaluation (SRE) data, the systems are analyzed in added noise conditions with a range of signal to noise ratios. Various experiments were conducted to study the influence of the noise on the speech activity detection and Wiener filtering in the front-end of the system.

Score Calibration in Face Recognition

An evaluation of the verification and calibration performance of a face recognition system based on inter-session variability modelling is presented. As an extension to calibration through linear transformation of scores, categorical calibration is introduced as a way to include additional information about images for calibration. The cost of likelihood ratio, which is a well-known measure in the speaker recognition field, is used as a calibration performance metric. The results obtained from the challenging mobile biometrics and surveillance camera face databases indicate that linearly calibrated face recognition scores are less misleading in their likelihood ratio interpretation than uncalibrated scores. In addition, the categorical calibration experiments show that calibration can be used not only to improve the likelihood ratio interpretation of scores, but also to improve the verification performance of a face recognition system.

Quality measures based calibration with duration and noise dependency for speaker recognition

Abstract This paper studies the effect of short utterances and noise on the performance of automatic speaker recognition. We focus on calibration aspects, and propose a calibration strategy that uses quality measures to model the calibration parameters. We carry out the proposed calibration by using simple Quality Measure Functions (QMFs) of duration and measured signal-to-noise-ratio from speech segments. We test the effectiveness of the approach using two databases, the development set of the I4U collaboration for the NIST Speaker Recognition Evaluation (SRE) 2012, and the evaluation test material of NIST SRE 2012 itself. In comparison with conventional linear calibration, results show that the proposed QMF approach successfully improves the system performance in terms of both discrimination and calibration.