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Third International Conference on Spoken Language Processing (ICSLP 94)Yokohama, Japan |
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In this work, we apply statistical trajectory models (STM's) to the task of phonetic recognition. STM's attempt to capture the dynamic characteristics and statistical dependencies of acoustic attributes in a segment-based framework. The approach is based on the creation of a track, fa, for each phonetic unit a. The track serves as a model of the dynamic trajectories of the acoustic attributes over the segment. The statistical framework for scoring incorporates the auto- and cross-correlation properties of the track error over time, within a segment. This paper presents the results of a series of phonetic recognition experiments using the timit acoustic-phonetic corpus [1]. Using the NIST train and core test sets we obtained context-independent and context-dependent recognition accuracies of 64.0% and 69.0% respectively.
Bibliographic reference. Goldenthal, William D. / Glass, James R. (1994): "Statistical trajectory models for phonetic recognition", In ICSLP-1994, 1871-1874.