A Probabilistic Approach to Confidence Estimation and Evaluation
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Neural - Network Based Measures of Confidence for Word Recognition
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
On using written language training data for spoken language modeling
HLT '94 Proceedings of the workshop on Human Language Technology
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
AUDIMUS.MEDIA: a broadcast news speech recognition system for the european portuguese language
PROPOR'03 Proceedings of the 6th international conference on Computational processing of the Portuguese language
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This article addresses error detection in broadcast news automatic transcription, as a post-processing stage. Based on the observation that many errors appear in bursts, we investigated the use of Markov Chains (MC) for their temporal modelling capabilities. Experiments were conducted on a large Amercian English broadcast news corpus from NIST. Common features in error detection were used, all decoder-based. MC classification performance was compared with a discriminative maximum entropy model (Maxent), currently used in our in-house decoder to estimate confidence measures, and also with Gaussian Mixture Models (GMM). The MC classifier obtained the best results, by detecting 16.2% of the errors, with the lowest classification error rate of 16.7%. To be compared with the GMM classifier, MC allowed to lower the number of false detections, by 23.5% relative. The Maxent system achieved the same CER, but detected only 7.2% of the errors.