Recognition of greek phonemes using support vector machines
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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This paper presents work on language identification research using conversational speech (the LDC Conversational Telephone Speech Database). The baseline system used in this study is based on language-dependent phone recognition and phonotactic constraints. The system was trained using monologue data and obtained an error rate of around 9% on a commonly used nine-language monologue test set. While the system was used to process conversational speech from the same nine-language task, dramatic performance degradation (with an error rate of 40%) was observed. Based on our analysis of conversational speech, two methods: (1) pre-processing and, (2) post-processing, were proposed. Without the presence of training data from conversational speech database, the final system (the baseline system enhanced by the two proposed methods) obtained an error rate of 24%, a substantial improvement (with 41% error reduction) compared with the baseline system.