Self-Organized Clustering for Feature Mapping in Language Recognition
Chang Huai You, Kong Aik Lee, Bin Ma, Haizhou Li · 2008
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-of-utterance information of the training databases, the feature mapping parameters are trained for each of the target languages. During recognition, the test utterance is identified 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.