Landmark detection for distinctive feature-based speech recognition

This work is a component of a proposed knowledge-based speech recognition system, which uses landmarks to guide the search for distinctivefeatures. In an utterance, landmarks identify localized regions where the acoustic manifestations of the linguistically motivated distinctive features are most salient. An algorithm for automatically detecting landmarks implemented by abrupt articulatory movements is described. The algorithm is a hierarchically structured algorithm rooted in linguistic and production theory. It looks for abrupt energy changes in six frequency bands and at two levels of temporal resolution. Landmarks are found based on information about which bands contain the abrupt change, the steady-state quality in the vicinity of the proposed landmark, segmental duration constraints, and an articulatory continuity requirement. Tested on a database of continuous speech recorded in a silent room by male and female speakers, the landmark detector is shown to perform well, with a 94% true detection rate and a 10% false detection rate. Most of the missed landmarks were adjacent to reduced vowels. These promising results indicate that landmarks are robustly identifiable points in the speech waveform and that a landmark detector as a front end in a speech recognition system is feasible. Work supported by NSF.