Conference paper
Towards PLDA-RBM based speaker recognition in mobile environment: Designing stacked/deep PLDA-RBM systems
The vast majority of text-independent speaker recognition systems rely on intermediate-sized vectors (i-vectors), which are compared by probabilistic linear discriminant analysis (PLDA). This paper proposes a PLDA-alike approach with restricted Boltzmann machines for i-vector based speaker recognition: two deep architectures are presented and examined, which aim at suppressing channel effects and recovering speaker-discriminative information on back-ends trained on a small dataset.
Experiments are carried out on the MOBIO SRE'13 database, which is a challenging and publicly available dataset for mobile speaker recognition with limited amounts of training data. The experiments show that the proposed system outperforms the baseline i-vector/PLDA approach by relative gains of 31% on female and 9% on male speakers in terms of half total error rate.
Language: | English |
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Publisher: | IEEE |
Year: | 2016 |
Pages: | 5055-5059 |
Proceedings: | 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing |
Series: | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
ISBN: | 1479999873 , 1479999881 , 147999989X , 147999989x , 9781479999873 , 9781479999880 and 9781479999897 |
ISSN: | 2379190x and 15206149 |
Types: | Conference paper |
DOI: | 10.1109/ICASSP.2016.7472640 |