This study presents our recent advances in our spoken document retrieval (SDR) system SpeechFind including our partnership with the Collaborative Digitization Program (CDP). A proto-type of SpeechFind for the CDP is currently serving as the search engine for 1,300 hours of the CDP audio content. These audio corpus of spoken document possess a wide range of conditions which make speech recognition challenging for reliable transcripts. In this paper, a reliability estimation method for the ASR-generated transcripts is proposed to provide more effective retrieval information for SpeechFind. The proposed estimator is based on Bayesian classification employing several confidence measures. We also propose a novel confidence measure for reliability estimation employing acoustically discriminative sub-word models. Experimental results on CDP material demonstrate that the proposed confidence measure is effective in improving the reliability estimator. By employing the proposed confidence measure based on discriminative model, 10.5% and 20.9% relative improvements were obtained in accuracy and critical error respectively.
Bibliographic reference. Kim, Wooil / Hansen, John H. L. (2007): "Advances in speechfind: transcript reliability estimation employing confidence measure based on discriminative sub-word model for SDR", In INTERSPEECH-2007, 2409-2412.