Text-Independent Speaker Verification Based on Information Theoretic Learning
In this paper VQ (Vector Quantization) based on information theoretic learning is investigated for the task of text-independent speaker verification. A novel VQ method based on the IT (Information Theoretic) principles is used for the task of speaker verification and compared with two classical V...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Mehran University of Engineering and Technology
2011-07-01
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| Series: | Mehran University Research Journal of Engineering and Technology |
| Subjects: | |
| Online Access: | http://publications.muet.edu.pk/research_papers/pdf/pdf140.pdf |
| Summary: | In this paper VQ (Vector Quantization) based on information theoretic learning is
investigated for the task of text-independent speaker verification. A novel VQ method
based on the IT (Information Theoretic) principles is used for the task of speaker
verification and compared with two classical VQ approaches: the K-means algorithm
and the LBG (Linde Buzo Gray) algorithm. The paper provides a theoretical background
of the vector quantization techniques, which is followed by experimental results
illustrating their performance. The results demonstrated that the ITVQ (Information
Theoretic Vector Quantization) provided the best performance in terms of classification
rates, EER (Equal Error Rates) and the MSE (Mean Squared Error) compare to Kmeans
and the LBG algorithms. The outstanding performance of the ITVQ algorithm
can be attributed to the fact that the IT criteria used by this algorithm provide superior
matching between distribution of the original data vectors and the codewords. |
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| ISSN: | 0254-7821 2413-7219 |