Abstract
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.
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Pellegrini, T., Trancoso, I. (2011). Error Detection in Broadcast News ASR Using Markov Chains. In: Vetulani, Z. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2009. Lecture Notes in Computer Science(), vol 6562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20095-3_6
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DOI: https://doi.org/10.1007/978-3-642-20095-3_6
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