MITLL 2012 speaker recognition evaluation system description
In NIST speaker recognition evaluation
Abstract
First attempt at Boltzmann machines for speaker recognition
In Odyssey speaker and language recognition workshop
Abstract Frequently organized by NIST, Speaker Recognition evaluations (SRE) show high accuracy rates. This demonstrates that this field of research is mature. The latest progresses came from the proposition of low dimensional i-vectors representation and new classifiers such as Probabilistic Linear Discriminant Analysis (PLDA) or Cosine Distance classifier. In this paper, we study some variants of Boltzmann Machines (BM). BM is used in image processing but still unexplored in Speaker Verification (SR).
A Channel-Blind System for Speaker Verification
In International conference on acoustics, speech and signal processing (ICASSP)
Abstract
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.