This paper addresses the problem of integrating speech and text content sources for the document search problem, as well as its usefulness from an ad-hoc retrieval -keyword search - point of view. Position specific posterior latices (PSPL) is naturally extended to deal with both speech and text content, where a new relevance ranking framework is proposed for integrating the different sources of information available. Experimental results on the MIT iCampus corpus show a relative improvement of 302% in mean average precision (MAP) when using speech content and metadata as opposed to just metadata (which constitutes about 1% of the amount of words in the transcription of the speech content).
['Jorge F. Silva', 'Ciprian Chelba', 'Alex Acero']
INTEGRATION OF METADATA IN SPOKEN DOCUMENT SEARCH USING POSITION SPECIFIC POSTERIOR LATICES