Abstract
To minimize the outliers’ effects, in this paper, a new speaker identification scheme based on the t-distribution mixture model is proposed. Since the t-distribution provides a longer and heavier tailed alternative to the Gaussian distribution, the mixture model with multivariate t-distribution is expected to show more robust results than the Gaussian mixture model(GMM) in the cases where outliers exist. In experiments, we compared the performance of the proposed scheme with that of using the conventional GMM to show its robustness.
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Lee, Y., Hahn, H., Han, Y., Lee, J. (2005). Robust Speaker Identification Based on t-Distribution Mixture Model. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_105
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DOI: https://doi.org/10.1007/11589990_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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