In this paper we present an approach for modeling and recognizing out-of-vocabulary (OOV) words in a single stage recognizer. A word-based recognizer is augmented with an extra OOV word model, which enables the OOV word to be predicted by a wordbased language model. The OOV model itself is phone-based, so that an OOV word can be realized as an arbitrary sequence of phones. A phone bigram is used to provide phonotactic constraints within the OOV model. A recognizer with this configuration can recognize words in the original vocabulary as well as any potential new words of arbitrary pronunciation. In our preliminary investigation of this framework, we have evaluated the recognizer on a weather information domain with one test set containing only in-vocabulary (IV) data, and another containing OOV words. On the IV test set, the recognizer had an OOV insertion rate of only 1.3%, and degraded the baseline WER from 10.4% to 10.7%. On the OOV test set, the recognizer was able to detect nearly half of the OOV words (47% detection rate).
Cite as: Bazzi, I., Glass, J.R. (2000) Modeling out-of-vocabulary words for robust speech recognition. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 1, 401-404, doi: 10.21437/ICSLP.2000-99
@inproceedings{bazzi00_icslp,
author={Issam Bazzi and James R. Glass},
title={{Modeling out-of-vocabulary words for robust speech recognition}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 1, 401-404},
doi={10.21437/ICSLP.2000-99}
}