Applying the Naïve Bayes Classifier to Assist Users in Detecting Speech Recognition Errors

Authors:
Lina Zhou;Jinjuan Feng;Andrew Sears;Yongmei Shi
Affiliations:
UMBC, Baltimore, MD;UMBC, Baltimore, MD;UMBC, Baltimore, MD;UMBC, Baltimore, MD
Venue:
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 07
Year:
2005

Citing 0
Cited 3

Semantic similarity for detecting recognition errors in automatic speech transcripts

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Enhancing accessibility through correction of speech recognition errors

ACM SIGACCESS Accessibility and Computing - ASSETS 2007 doctoral consortium
Efficiency of speech recognition for using interface design environments by novel designers

AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7

Quantified Score

Hi-index	0.00

Visualization

Abstract

Speech recognition (SR) is a technology that can improve accessibility to computer systems for people with physical disabilities or situation-introduced disabilities. The wide adoption of SR technology; however, is hampered by the difficulty in correcting system errors. HCI researchers have attempted to improve the error correction process by employing multi-modal or speech-based interfaces. There is limited success in applying raw confidence scores (indicators of system's confidence in an output) to facilitate anchor specification in the navigation process. This paper applies a machine learning technique, in particular Naïve Bayes classifier, to assist detecting dictation errors. In order to improve the generalizability of the classifiers, input features were obtained from generic SR output. Evaluation on speech corpuses showed that the performance of Naïve Bayes classifier was better than using raw confidence scores.