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• W2100551412 abstract "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 method 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. >" @default.
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• W2100551412 date "2002-12-04" @default.
• W2100551412 modified "2023-09-30" @default.
• W2100551412 title "Allophone clustering for continuous speech recognition" @default.
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• W2100551412 doi "https://doi.org/10.1109/icassp.1990.115900" @default.
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