EMPLOYMENT OF SUBSPACE GAUSSIAN MIXTURE MODELS IN SPEAKER RECOGNITION
| Type of publication: | Conference paper |
| Citation: | Motlicek_ICASSP_2015 |
| Publication status: | Published |
| Booktitle: | 2015 IEEE International Conference on Acoustics, Speech, and Signal Processing |
| Year: | 2015 |
| Month: | May |
| Pages: | 4445-4449 |
| Location: | Brisbane, Australia |
| Organization: | IEEE |
| ISBN: | 978-1-4673-6996-1 |
| Crossref: | Motlicek_Idiap-RR-16-2015: |
| URL: | http://icassp2015.org/... |
| Abstract: | This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significantly outperform traditional HMM/GMMs in Automatic Speech Recognition (ASR) applications. An extension to the basic SGMM framework allows to robustly estimate low-dimensional speaker vectors and exploit them for speaker adaptation. We propose a speaker verification framework based on low-dimensional speaker vectors estimated using SGMMs, trained in ASR manner using manual transcriptions. To test the robustness of the system, we evaluate the proposed approach with respect to the state-of-the-art i-vector extractor on the NIST SRE 2010 evaluation set and on four different length-utterance conditions: 3sec-10sec, 10 sec-30 sec, 30 sec-60 sec and full (untruncated) utterances. Experimental results reveal that while i-vector system performs better on truncated 3sec to 10sec and 10 sec to 30 sec utterances, noticeable improvements are observed with SGMMs especially on full length-utterance durations. Eventually, the proposed SGMM approach exhibits complementary properties and can thus be efficiently fused with i-vector based speaker verification system. |
| Keywords: | |
| Projects |
Idiap SIIP |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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