This paper presents a combination of out-of-vocabulary (OOV) word modeling and rejection techniques in an attempt to accept utterances embedding a keyword and reject utterances with nonkeywords. The goal of this research is to develop a robust, task-independent Spanish keyword spotter and to develop a method for optimizing confidence thresholds for a particular context. To model OOV words, we employed both word and sub-word units as fillers, combined with n-gram language models. We also introduce a methodology for optimizing confidence thresholds to control the tradeoffs between acceptance, confirmation, and rejection of utterances. Our experiments are based on a Mexican Spanish auto-attendant system using the SpeechWorks recognizer release 6.5 Second Edition, in which we achieved a reduction in error of 8.9% as compared to the baseline system. Most of the error reduction is attributed to better keyword detection in utterances that contain both keywords and OOV words.
CITATION STYLE
Cuayáhuit, H., & Serridge, B. (2002). Out-of-vocabulary word modeling and rejection for Spanish keyword spotting systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2313, pp. 156–165). Springer Verlag. https://doi.org/10.1007/3-540-46016-0_17
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