Joint Factor Analysis Model Research Articles

Text-Dependent Speaker Recognition With Random Digit Strings

In this paper, we explore joint factor analysis (JFA) for text-dependent speaker recognition with random digit strings. The core of the proposed method is a JFA model by which we extract features. These features can either represent overall utterances or individual digits, and are fed into a trainable backend to estimate likelihood ratios. Within this framework, several extensions are proposed. First is a logistic regression method for combining log-likelihood ratios that correspond to individual mixture components. Second is the extraction of phonetically aware Baum--Welch statistics, by using forced alignment instead of the typical posterior probabilities that are derived by the universal background model. We also explore a digit-string-dependent way to apply score normalization that exhibits a notable improvement compared to the standard one. By fusing six JFA features, we attained 2.01% and 3.19% equal error rates on male and female, respectively, on the challenging RSR2015 (part III) dataset.

A Study of Interspeaker Variability in Speaker Verification

We propose a new approach to the problem of estimating the hyperparameters which define the interspeaker variability model in joint factor analysis. We tested the proposed estimation technique on the NIST 2006 speaker recognition evaluation data and obtained 10%-15% reductions in error rates on the core condition and the extended data condition (as measured both by equal error rates and the NIST detection cost function). We show that when a large joint factor analysis model is trained in this way and tested on the core condition, the extended data condition and the cross-channel condition, it is capable of performing at least as well as fusions of multiple systems of other types. (The comparisons are based on the best results on these tasks that have been reported in the literature.) In the case of the cross-channel condition, a factor analysis model with 300 speaker factors and 200 channel factors can achieve equal error rates of less than 3.0%. This is a substantial improvement over the best results that have previously been reported on this task.