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- W2061520989 abstract "This talk presents phonetic models that capture both the dynamic characteristics and the statistical dependencies of acoustic attributes in a segment-based framework. The approach is based on the creation of a track, Tα, for each phonetic unit α. 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. On a vowel classification task [W. Goldenthal and J. Glass, ‘‘Modeling Spectra Dynamics for Vowel Classification,’’ Proc. Eurospeech 93, pp. 289–292, Berlin, Germany (1993)], this methodology achieved classification performance of 68.9%. This result compares favorably with other studies using the timit corpus. This talk extends this result by presenting context-independent and context-dependent experiments for all the phones. Context-independent classification performance of 76.8% is demonstrated. The key to implementing the context-dependent classifier consists of merging tracks trained separately on left and right contexts to synthesize any desired context during classification. This method allows one to synthesize a track for triphone contexts not seen in the training set. Using a total of 4167 gender-dependent biphone tracks, 58 phonetic statistical models, and no phone grammar, a context-dependent classification performance of 80.5% was achieved. This result increases to 85.8% when a trigram phone grammar is added." @default.
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- W2061520989 date "1994-05-01" @default.
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- W2061520989 title "Statistical trajectory models for phonetic classification" @default.
- W2061520989 doi "https://doi.org/10.1121/1.409413" @default.
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