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- W1956550074 abstract "This paper describes progress on a commercial application of the MECS recognition system to the task of recognizing Japanese family names spoken by customers into the answering machines of a large marketing/human resource company. The task is thus speaker-independent, open vocabulary, and is characterized by large variation in caller speaking styles, telephone types and acoustic environments. Our results show that context-independent hidden Markov models trained discriminatively with the minimum classification error criterion are a practical alternative to context-dependent models based on phonetic decision trees, yielding better performance with a much smaller number of parameters. On this difficult task we have obtained 59% correct family name recognition. A phoneme-based confidence measure enables us to obtain 85% correct name recognition for accepted utterances, at an overall utterance acceptance rate of 15%." @default.
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- W1956550074 date "2002-11-07" @default.
- W1956550074 modified "2023-09-30" @default.
- W1956550074 title "Discriminative training for large vocabulary telephone-based name recognition" @default.
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- W1956550074 doi "https://doi.org/10.1109/icassp.2000.860215" @default.
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