Collecting training data for real-world text-independent speaker recognition is challenging. In practice, utterances for a specific speaker are often mixed with many other acoustic signals. To guarantee the recognition performance, the segments spoken by target speakers should be precisely picked out. An automatic detection could be developed to reduce the cost of expensive human hand-made anno...

A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...

In speaker recognition, it is very important to use individual information extracted from speech waves. Most of the speaker recognition methods assume that each part of speech has equal amount of information to represent a speaker, although it di erently contribute to speaker recognition. The aim of this paper is to suggest a new scoring method of the HMM, which applies di erent importance to a...

This paper investigates the effect of a transfer functionbased voice transformation on automatic speaker recognition system performance. We focus on increasing the impostor acceptance rate, by modifying the voice of an impostor in order to target a specific speaker. This paper follows previous works where we demonstrate that, if someone has a knowledge on the speaker recognition method used, it...

Unseen handset mismatch is the major source of performance degradation for speaker recognition in telecommunication environment since handset distortions are tightly coupled with speaker characteristics. In this paper, a soft-decision unseen handset characteristics estimation method based on a priori knowledge interpolation is proposed to decouple the characteristics of the unseen handset and s...

This paper presents a novel self-contained two-wire speaker recognition framework. The classical approach to two-wire speaker recognition usually requires a preliminary explicit speaker segmentation stage in order to extract audio files for the two hypothesized speakers. We propose an implicit speaker segmentation method implemented at the supervector level of speaker recognition systems. By pe...

Nowadays, almost all speaker-independent (SI) speech recognition systems use CDHMM with multivariate mixture Gaussian as observation density to cover speaker variabilities. It has been shown that given sufficient training data, the more mixtures are used in the HMM observation density, the better the system’s perform. However, acoustic HMM with more Gaussian densities is more complex and slows ...