@mastersthesis{Kazemian:2009:thesis,
  author = "Siavash Kazemian",
  title = "A Critical Assessment of Spoken Utterance Retrieval through Approximate Lattice Representations",
  year = "2009",
  school = "Department of Computer Science, University of Toronto",
  month = "January",
  abstract = "This paper compares the performance of Position-specific Posterior Lattices
              (PSPL) and Confusion Networks (CN) applied to Spoken Utterance
              Retrieval, and tests these recent proposals against several baselines,
              namely 1-best transcription, using the whole lattice, and the set-of-words
              baseline. The set-of-words baseline is used for the first time in context of
              Spoken Utterance Retrieval. PSPL and CN provide compact representations
              that generalize the original segment lattices and provide greater
              recall 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. While in the low-WER environments all methods
              tested have comparable performance, both PSPL and CN significantly
              outperform the 1-best transcription in high-WER environments but perform
              similarly to the whole lattice and set-of-words baselines.",
  download = "http://ftp.cs.toronto.edu/pub/gh/Kazemian-MSc-paper.pdf"
}