Front-End Factor Analysis For Speaker Verification
In IEEE Transactions on Audio, Speech, and Language Processing
Abstract
Cosine similarity scoring without score normalization techniques
In Odyssey the speaker and language recognition
Abstract
Unsupervised speaker adaptation based on the cosine similarity for text-independent speaker verification
In Odyssey the speaker and language recognition
Abstract
LRDE Speaker Recognition System for NIST-SRE 2010
In NIST 2010 speaker recognition evaluation
Abstract
Cepstral and long-term features for emotion recognition
In Interspeech
Abstract In this paper, we describe systems that were developed for the Open Performance Sub-Challenge of the INTERSPEECH 2009 Emotion Challenge. We participate to both two-class and five-class emotion detection. For the two-class problem, the best performance is obtained by logistic regression fusion of three systems. Theses systems use short- and long-term speech features. This fusion achieved an absolute improvement of 2,6% on the unweighted recall value compared with [6]. For the five-class problem, we submitted two individual systems: cepstral GMM vs.
Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification
In Interspeech
Abstract This paper presents a new speaker verification system architecture based on Joint Factor Analysis (JFA) as feature extractor. In this modeling, the JFA is used to define a new low-dimensional space named the total variability factor space, instead of both channel and speaker variability spaces for the classical JFA. The main contribution in this approach, is the use of the cosine kernel in the new total factor space to design two different systems: the first system is Support Vector Machines based, and the second one uses directly this kernel as a decision score.
Support vector machines and joint factor analysis for speaker verification
In IEEE-ICASSP
Abstract This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied on different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that the use of JFA factors gave the best results especially when within class covariance normalization method is applied in the speaker factors space, in order to compensate for the channel effect.
Comparison between factor analysis and GMM support vector machines for speaker verification
In Proceedings of the speaker and language recognition workshop (IEEE-odyssey 2008)
Abstract We present a comparison between speaker verification systems based on factor analysis modeling and support vector machines using GMM supervectors as features. All systems used the same acoustic features and they were trained and tested on the same data sets. We test two types of kernel (one linear, the other non-linear) for the GMM support vector machines. The results show that factor analysis using speaker factors gives the best results on the core condition of the NIST 2006 speaker recognition evaluation.
Kernel combination for SVM speaker verification
In Proceedings of the speaker and language recognition workshop (IEEE-odyssey 2008)
Abstract We present a new approach for constructing the kernels used to build support vector machines for speaker verification. The idea is to construct new kernels by taking linear combination of many kernels such as the GLDS and GMM supervector kernels. In this new kernel combination, the combination weights are speaker dependent rather than universal weights on score level fusion and there is no need for extra-data to estimate them. An experiment on the NIST 2006 speaker recognition evaluation dataset (all trial) was done using three different kernel functions (GLDS kernel, linear and Gaussian GMM supervector kernels).