[A discussion of the technical issues involved in developing information retrieval systems for the spoken word] © IMAGESTATE Ever-increasing computing power and connectivity bandwidth, together with falling storage costs, are resulting in an overwhelming amount of data of various types being produced, exchanged, and stored. Consequently, information search and retrieval has emerged as a key application area. Text-based search is the most active area, with applications that range from Web and local network search to searching for personal information residing on one’s own hard-drive. Speech search has received less attention perhaps because large collections of spoken material have previously not been available. However, with cheaper storage and increased broadband access, there has been a subsequent increase in the availability of online spoken audio content such as news broadcasts, podcasts, and academic lectures.

This paper compares the performance of position-specific posterior lattices (PSPL) and confusion networks applied to spoken utterance retrieval, and tests these recent proposals against several baselines in two disparate domains. These lossy methods provide compact representations that generalize the original segment lattices and provide greater recall and robustness, but have yet to be evaluated against each other in multiple WER conditions for spoken utterance retrieval. Our comparisons suggest that while PSPL and confusion networks have comparable recall, the former is slightly more precise, although its merit appears to be coupled to the assumptions of low-frequency search queries and low-WER environments.