Query expansion for imperfect speech: Applications in distributed learning

Abstract

Advances in speech recognition technology have shown encouraging results for spoken document retrieval where the average precision often approaches 70% of that achieved for perfect text transcriptions. Typical applications of spoken document retrieval pertain to retrieval of stories from archived video/audio assets. In the CueVideo project, our application focus is spoken document retrieval from a video database for just-in-time training/distributed learning. Typical content is not pre-segmented, has no predefined structure, is of varying audio quality, and may not have domain specific data available. For such content, we propose a two level search, namely, a first level search across the entire video collection, and a second level search within a specific video. At both search levels, we perform an experimental evaluation of a combination of new and existing query expansion methods, intended to offset retrieval errors due to misrecognition.