{"id":155808,"date":"2005-06-01T00:00:00","date_gmt":"2005-06-01T00:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/position-specific-posterior-lattices-for-indexing-speech\/"},"modified":"2018-10-16T20:07:29","modified_gmt":"2018-10-17T03:07:29","slug":"position-specific-posterior-lattices-for-indexing-speech","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/position-specific-posterior-lattices-for-indexing-speech\/","title":{"rendered":"Position Specific Posterior Lattices for Indexing Speech"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The paper presents the Position Speci\ufb01c Posterior Lattice, a novel representation of automatic speech recognition lattices that naturally lends itself to ef\ufb01cient indexing of position information and subsequent relevance ranking of spoken documents using proximity.<\/p>\n<p>In experiments performed on a collection of lecture recordings \u2014 MIT iCampus data \u2014 the spoken document ranking accuracy was improved by 20% relative over the commonly used baseline of indexing the 1-best output from an automatic speech recognizer. The Mean Average Precision (MAP) increased from 0.53 when using 1-best output to 0.62 when using the new lattice representation. The reference used for evaluation is the output of a standard retrieval engine working on the manual transcription of the speech collection.<\/p>\n<p>Albeit lossy, the PSPL lattice is also much more compact than the ASR 3-gram lattice from which it is computed \u2014 which translates in reduced inverted index size as well \u2014 at virtually no degradation in word-error-rate performance. Since new paths are introduced in the lattice, the ORACLE accuracy increases over the original ASR lattice.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The paper presents the Position Speci\ufb01c Posterior Lattice, a novel representation of automatic speech recognition lattices that naturally lends itself to ef\ufb01cient indexing of position information and subsequent relevance ranking of spoken documents using proximity. 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