Error Approximation and Minimum Bayes Risk Acoustic Model Estimation

Add to your list(s) Download to your calendar using vCal

• Matt Gibson (University of Sheffield)
• Thursday 10 July 2008, 13:00-14:00
• LR5, Engineering Department, Baker Building.

If you have a question about this talk, please contact Dr Marcus Tomalin.

Acoustic model estimation using the Bayes risk criterion involves computation of the minimum edit (or Levenshtein) error between the correct sequence of words (or phonemes) and each member of a set of alternative hypotheses. In the context of large vocabulary speech recognition, the set of alternative hypotheses is large, rendering the computational side of this process prohibitively expensive. So practical implementations of Bayes risk minimization use two approximations.

The first approximation is to use a pruned set of alternative hypotheses instead of the full hypothesis space. The second is to use a computationally-inexpensive approximation to the Levenshtein error for each member of this pruned set.

This talk focuses on the latter approximation. A previously introduced approach (D. Povey, PhD thesis, 2003) uses alignments of the correct and alternative sequences to approximate the Levenshtein error between the sequences. We examine the accuracy of this approach, highlight some limitations, and propose alternative alignment-based approximations. We then present experimental work quantifying the impact of these alternatives upon MBR parameter re-estimation.

This talk is part of the Machine Intelligence Laboratory Speech Seminars series.

This talk is included in these lists:

• CUED Speech Group Seminars
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• Guy Emerson's list
• Information Engineering Division seminar list
• LR5, Engineering Department, Baker Building
• Machine Intelligence Laboratory Speech Seminars
• PhD related
• Trust & Technology Initiative - interesting events

Note that ex-directory lists are not shown.