Automatic Estimation of Word Significance oriented for Speech-based Information Retrieval
نویسندگان
چکیده
Automatic estimation of word significance oriented for speech-based Information Retrieval (IR) is addressed. Since the significance of words differs in IR, automatic speech recognition (ASR) performance has been evaluated based on weighted word error rate (WWER), which gives a weight on errors from the viewpoint of IR, instead of word error rate (WER), which treats all words uniformly. A decoding strategy that minimizes WWER based on a Minimum Bayes-Risk framework has been shown, and the reduction of errors on both ASR and IR has been reported. In this paper, we propose an automatic estimation method for word significance (weights) based on its influence on IR. Specifically, weights are estimated so that evaluation measures of ASR and IR are equivalent. We apply the proposed method to a speech-based information retrieval system, which is a typical IR system, and show that the method works well.
منابع مشابه
Minimum Bayes-Risk Decoding cons for Information Retrie
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 eval...
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