Interspeech'2005 - Eurospeech
The paper addresses a new evaluation measure of automatic speech recognition (ASR) and a decoding strategy oriented for speech-based information retrieval (IR). Although word error rate (WER), which treats all words in a uniform manner, has been widely used as an evaluation measure of ASR, significance of words are different in speech understanding or IR. In this paper, we define a new ASR evaluation measure, namely, weighted word error rate (WWER) that gives a weight on errors from a viewpoint of IR. Then, we formulate a decoding method to minimize WWER based on Minimum Bayes-Risk (MBR) framework, and show that the decoding method improves WWER and IR accuracy.
Bibliographic reference. Nanjo, Hiroaki / Misu, Teruhisa / Kawahara, Tatsuya (2005): "Minimum Bayes-risk decoding considering word significance for information retrieval system", In INTERSPEECH-2005, 561-564.