Estimation of articulatory gesture patterns from speech acoustics

摘要

We investigated dynamic programming (DP) and state-model (SM) approaches for estimating gestural scores from speech acoustics. We performed a word-identification task using the gestural pattern vector sequences estimated by each approach. For a set of 75 randomly chosen words, we obtained the best word-identification accuracy (66.67%) using the DP approach. This result implies that considerable support for lexical access during speech perception might be provided by such a method of recovering gestural information from acoustics.