This paper proposes an efficient confidence measure applied at the word level by combining various likelihood ratio tests. The estimates are derived from the local N-best sub-hypotheses. This approach allows the confidence measures to take into account the effect of neighboring words and still provides the estimate localized around the word to be verified. It produces an effective confidence measure that is usable for various tasks. We compared the results with other likelihood ratio based confidence measures including garbage model, N-best homogeneity and online garbage models. The proposed method gave more than 30% relative false accept rate reduction over other methods and the rejection performance was less task-dependent.

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Bibliographic reference.  Tan, Beng T. / Gu, Yong / Thomas, Trevor (2001): "Word level confidence measures using n-best sub-hypotheses likelihood ratio", In EUROSPEECH-2001, 2565-2568.