Automatic emotion recognition from speech a PhD research proposal
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Applying emotional factor analysis and I-vector to emotional speaker recognition
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Comparative evaluation of feature normalization techniques for speaker verification
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
Automatic speaker age and gender recognition using acoustic and prosodic level information fusion
Computer Speech and Language
Universal attribute characterization of spoken languages for automatic spoken language recognition
Computer Speech and Language
Is masking a relevant aspect lacking in MFCC? A speaker verification perspective
Pattern Recognition Letters
Multitaper MFCC and PLP features for speaker verification using i-vectors
Speech Communication
i-Vector with sparse representation classification for speaker verification
Speech Communication
Speaker verification in score-ageing-quality classification space
Computer Speech and Language
Compact bag-of-words visual representation for effective linear classification
Proceedings of the 21st ACM international conference on Multimedia
I-vector based speaker recognition using advanced channel compensation techniques
Computer Speech and Language
A study of voice activity detection techniques for NIST speaker recognition evaluations
Computer Speech and Language
A Study of the Cosine Distance-Based Mean Shift for Telephone Speech Diarization
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Factorized Sub-Space Estimation for Fast and Memory Effective I-vector Extraction
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.