ISCA Archive ISCSLP 2008
ISCA Archive ISCSLP 2008

Self-organized Clustering for Feature Mapping in Language Recognition

Chang-Huai You, Kong-Aik Lee, Bin Ma, Hai-Zhou Li

In this paper, we propose a self-organized clustering method for feature mapping to compensate the channel variation in spoken language recognition. The self-organized clustering is realized by transforming the utterances into the Gaussian mixture model (GMM) supervectors and categorizing the supervectors through k-mean algorithm. Based on the language-dependent cluster-ofutterance information of the training databases, the feature mapping parameters are trained for each of the target languages. During recognition, the test utterance is identiļ¬ed to be one of the clusters according to the feature mapping parameters and then transformed into the cluster-independent features through feature mapping for a given target language. We show the effectiveness of the proposed self-organized feature mapping scheme through the 2003 National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) by using GMM recognizer.


Cite as: You, C.-H., Lee, K.-A., Ma, B., Li, H.-Z. (2008) Self-organized Clustering for Feature Mapping in Language Recognition. Proc. International Symposium on Chinese Spoken Language Processing, 177-180

@inproceedings{you08_iscslp,
  author={Chang-Huai You and Kong-Aik Lee and Bin Ma and Hai-Zhou Li},
  title={{Self-organized Clustering for Feature Mapping in Language Recognition}},
  year=2008,
  booktitle={Proc. International Symposium on Chinese Spoken Language Processing},
  pages={177--180}
}