{"id":160725,"date":"2011-11-01T00:00:00","date_gmt":"2011-11-01T07:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/calibration-of-confidence-measures-in-speech-recognition\/"},"modified":"2018-10-16T21:57:19","modified_gmt":"2018-10-17T04:57:19","slug":"calibration-of-confidence-measures-in-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/calibration-of-confidence-measures-in-speech-recognition\/","title":{"rendered":"Calibration of Confidence Measures In Speech Recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Most speech recognition applications in use today rely heavily on confidence measure for making optimal decisions. In this work, we aim to answer the question: what can be done to improve the quality of confidence measure if we cannot modify the speech recognition engine? The answer provided in this paper is a post-processing step called confidence calibration, which can be viewed as a special adaptation technique applied to confidence measure. Three confidence calibration methods have been developed in this work: the maximum entropy model with distribution constraints, the artificial neural network, and the deep belief network. We compare these approaches and demonstrate the importance of key features exploited: the generic confidence-score, the application-dependent word distribution, and the rule coverage ratio. We demonstrate the effectiveness of confidence calibration on a variety of tasks with significant normalized cross entropy increase and equal error rate reduction.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Most speech recognition applications in use today rely heavily on confidence measure for making optimal decisions. In this work, we aim to answer the question: what can be done to improve the quality of confidence measure if we cannot modify the speech recognition engine? The answer provided in this paper is a post-processing step called [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"dongyu"},{"type":"user_nicename","value":"jinyli"},{"type":"user_nicename","value":"deng"}],"msr_publishername":"IEEE SPS","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Transactions on Audio, Speech, and Language Processing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"IEEE Transactions on Audio, Speech, and Language 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