SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Compensation of Nuisance Factors for Speaker and Language Recognition
IEEE Transactions on Audio, Speech, and Language Processing
A Vector Space Modeling Approach to Spoken Language Identification
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we try to introduce the idea of total variability used in speaker recognition to language recognition. In language total variability (LTV), we propose a new recognition system names language total variability recognition system. Our experiments show that language total factor vector includes the language dependent in- formation. What's more, our experiments show that language total factor vector contains different language dependent information. Ex- periment results on 2007 National Institute of Standards and Tech- nology (NIST) Language Recognition Evaluation (LRE) databases show our proposed system LTV can achieve performance similar to that obtained with state-of-the-art approaches, and we can obtain fur- ther improvement by combining the new system with state-of-the-art systems. It leads to relative improvement of 5.7% in EER and 13.2% in minDCF comparing with the performance of the combination of the MMI and the GMM-SVM systems.