John-Mark Bell
Abstract
The learning within lectures of hearing-impaired students can be hindered by errors in captions generated by speech recognition. My research intends to address this problem by investigating ways of correcting these captions. I summarise approaches to automatic error correction and describe the preliminary studies that have been conducted. These studies show that human editors set a tough benchmark for automatic correction to meet and indicate that automatic correction is feasible. Finally, I summarise my intention to develop a correction framework that will permit quantitative and qualitative testing of correction methods.
- K. Bain, S. Basson, A. Faisman, and D. Kanevsky. Accessibility, transcription and access everywhere. IBM Systems Journal, 44(3):589--603, July 2005. Google Scholar
Digital Library
- C. Bousquet-Vernhettes, R. Privat, and N. Vigouroux. Error handling in spoken dialogue systems: toward corrective dialogue. In Proceedings of Workshop on Error Handling in Spoken Dialogue Systems, 2003.Google Scholar
- F. Casacuberta. Inference of finite-state transducers by using regular grammars and morphisms. In Grammatical Inference: Algorithms and Applications, volume 1891 of Lecture Notes in Computer Science, pages 1--14. Springer-Verlag, 2000. Google ScholarDigital Library
- H. Lieberman, A. Faaborg, W. Daher, and J. Espinosa. How to wreck a nice beach you sing calm incense. In Proceedings of the 10th International Conference on Intelligent User Interfaces, pages 278--280, New York, NY, 2005. Google ScholarDigital Library
- E. K. Ringger and J. F. Allen. Error correction via a post-processor for continuous speech recognition. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, volume 1, pages 427--430, Atlanta, GA, 1996. Google ScholarDigital Library
- A. Sarma and D. D. Palmer. Context-based speech recognition error detection and correction. In Proceedings of the HLT-NAACL Conference: Short Papers, pages 85--88, Boston, MA, 2004. Google ScholarDigital Library
- A. Setlur, R. Sukkar, and J. Jacob. Correcting recognition errors via discriminative utterance verification. In Proceedings., Fourth International Conference on Spoken Language, volume 2, pages 602--605, October 1996.Google ScholarCross Ref
- M. Wald, J.-M. Bell, P. Boulain, K. Doody, and J. Gerrard. Correcting automatic speech recognition errors in real time. International Journal of Speech Technology, In Press.Google Scholar
- L. Zhou, J. Feng, A. Sears, and Y. Shi. Applying the naive bayes classifier to assist users in detecting speech recognition errors. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pages 183b--183b, 2005. Google ScholarDigital Library
Index Terms
- Enhancing accessibility through correction of speech recognition errors
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