Allophone clustering for continuous speech recognition

Two methods are presented for subword clustering. The first method is an agglomerative clustering algorithm. This method is completely data-driven and finds clusters without any external guidance. The second method uses decision trees for clustering. This method uses an expert-generated list of questions about contexts and recursively selects the most appropriate question to split the allophones. Preliminary results showed that when the training set has a good coverage of the allophonic variations in the test set, both methods are capable of high-performance recognition. However, under vocabulary-independent conditions, the method using tree-based allophones outperformed agglomerative clustering because of its superior generalization capability.