This paper introduces the theory of factor analysis of the mixture of Auto-Associative Neural Networks (AANNs) with application in speaker verification. First, we formulate the problem of learning a low-dimensional subspace in part of the mixture of AANNs parameter space, and subsequently derive the update equations by minimizing loss function of the mixture. Second, we apply this technique to ...

Recent performance gains in speaker verification systems suggest it is now viable to employ these systems in open-set speaker identification applications where an automated decision is passed to a human-in-the-loop for final analysis and decision. This paper examines the performance for when a speaker verification system expands into the identification domain. Our results indicate that separate...

In this paper we propose a multifactor hybrid fusion approach for enhancing security in audio-visual speaker verification. Speaker verification experiments conducted on two audiovisual databases, VidTIMIT and UCBN, show that multifactor hybrid fusion involve a combination feature-level fusion of lip-voice features and face-lip-voice features at score-level is indeed a powerful technique for spe...

In multi-speaker scenarios, speech processing tasks like speaker identification and recognition are susceptible to noise overlapped voices. As the voices a complicated mixture of signals, target extraction method from this is good front end solution for further understanding classifying. The quality separation can be assessed by ratio or subjective scoring also accuracy downstream identificatio...

This report describes a powerful channel compensation method for the text-independent speaker verification task. This powerful method is developed in the LRDE Speaker Verification framework. The purpose of a text-independent speaker verification system is to check whether a hypothesised speaker is really the author of a speech utterance. The channel compensation problem arises when training dat...

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiment...

A method for blind estimation of DET curves for speaker verification systems is proposed. Verification error probabilities are estimated on a database where speaker identities are unknown. The database must provide a set of impostor-only tests as well as a set of mixed impostor and target tests. This method is tested on 9 speaker verification systems that were scored on the NIST 2000 database. ...