Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition


The KIPS Transactions:PartB , Vol. 12, No. 4, pp. 437-442, Aug. 2005
10.3745/KIPSTB.2005.12.4.437,   PDF Download:

Abstract

For the purpose of speaker normalization in speaker independent speech recognition systems, experiments are conducted on a method based on Gaussian mixture model(GMM). The method, which is an improvement of the previous study based on vector quantizer, consists of modeling the probability distribution of canonical feature vectors by a GMM with an appropriate number of clusters, and of estimating the warp factor of atest speaker by making use of the obtained probabilistic model. The purpose of this study is twofold : improving he existing ML based methods, and comparing the performance of what is called ´soft decision´ method with that of the previous study based on vector quantizer. The effectiveness of the proposed method is investigated by recognition experiments on the TIMIT corpus. The experimental results showed that a little improvement could be obtained by adjusting the number of clusters in GMM appropriately.


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Cite this article
[IEEE Style]
O. K. Shin, "Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition," The KIPS Transactions:PartB , vol. 12, no. 4, pp. 437-442, 2005. DOI: 10.3745/KIPSTB.2005.12.4.437.

[ACM Style]
Ok Keun Shin. 2005. Speaker Normalization using Gaussian Mixture Model for Speaker Independent Speech Recognition. The KIPS Transactions:PartB , 12, 4, (2005), 437-442. DOI: 10.3745/KIPSTB.2005.12.4.437.