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Hybrid language models for out of vocabulary word detection in large vocabulary conversational speech recognition

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2 Author(s)
Yazgan, A. ; Center for Language & Speech Process., Johns Hopkins Univ., Baltimore, MD, USA ; Saraclar, M.

In this paper, we propose a method for out-of-vocabulary (OOV) word detection and take a step toward open vocabulary automatic speech recognition. The proposed method uses a hybrid language model combining words and subword units such as phones or syllables. We describe a detection algorithm based on the posterior count of the OOV words given the hybrid model, and compare it to using the posterior probability of the best word string given a conventional word only model. Experimental results on the Switchboard corpus are presented for different vocabulary sizes. The new method yields a gain of over 10% in OOV word detection. In addition, a modest number of the OOV word pronunciations are found correctly.

Published in:

Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on  (Volume:1 )

Date of Conference:

17-21 May 2004