Soft indexing of speech content for search in spoken documents

The paper presents the Position Specific Posterior Lattice (PSPL), a novel lossy representation of automatic speech recognition lattices that naturally lends itself to efficient indexing and subsequent relevance ranking of spoken documents. This technique explicitly takes into consideration the content uncertainty by means of using soft-hits. Indexing position information allows one to approximate N-gram expected counts and at the same time use more general proximity features in the relevance score calculation. In fact, one can easily port any state-of-the-art text-retrieval algorithm to the scenario of indexing ASR lattices for spoken documents, rather than using the 1-best recognition result. Experiments performed on a collection of lecture recordings—MIT iCampus database—show that the spoken document ranking performance was improved by 17–26% relative over the commonly used baseline of indexing the 1-best output from an automatic speech recognizer (ASR). The paper also addre...

• Type: Journal
• Year: 2007
• Where: CSL
• Authors: Ciprian Chelba, Jorge Silva, Alex Acero
• Keywords: Automated Reasoning, Automatic Speech, CSL 2007, Speech Content, Spoken Document