{"id":162087,"date":"2008-04-01T00:00:00","date_gmt":"2008-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/template-constrained-posterior-for-verifying-phone-transcriptions\/"},"modified":"2018-10-16T20:08:59","modified_gmt":"2018-10-17T03:08:59","slug":"template-constrained-posterior-for-verifying-phone-transcriptions","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/template-constrained-posterior-for-verifying-phone-transcriptions\/","title":{"rendered":"Template Constrained Posterior for Verifying Phone Transcriptions"},"content":{"rendered":"<p>A new statistical confidence measure, Template Constrained Posterior (TCP), is proposed for verifying phone transcriptions of speech databases. Different from generalized posterior probability (GPP), TCP is computed by considering string hypotheses that bear a focused unit, e.g., phone with partially matched left and right contexts. Parameters used for TCP include context window length, partial matching ratio, KLD threshold for selecting confusable phones, and verification threshold. They are determined by minimizing verification errors in a development set. Evaluated on a test set which contains 52.1% sentence errors and 0.62% phone errors, TCP achieves 92% and 88% error hit rate in rejected sentences, when the corresponding acceptance ratios are set at 90% and 80%, respectively.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A new statistical confidence measure, Template Constrained Posterior (TCP), is proposed for verifying phone transcriptions of speech databases. Different from generalized posterior probability (GPP), TCP is computed by considering string hypotheses that bear a focused unit, e.g., phone with partially matched left and right contexts. Parameters used for TCP include context window length, partial matching [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162087","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"ICASSP 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