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 distinctive features. In the speech signal, landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. This paper describes an algorithm for automatically detecting acoustically abrupt landmarks. Some examples of acoustically abrupt landmarks are stop closures and releases, nasal closures and releases, and the point of cessation of free vocal fold vibration due to a velopharyngeal port closure at a nasal-to-obstruent juncture. As a consequence of landmark detection, the algorithm provides estimates of the broad phonetic class (articulator-free features) of the underlying segment. The algorithm is hierarchically structured, and is rooted in linguistic and speech production theory. It uses several factors to detect landmarks: energy abruptness in five frequency bands and at two levels of temporal resolution, segmental duration, broad phonetic class constraints, and articulatory constraints. Tested on a database of continuous, clean speech of women and men, the landmark detector has detection rates over 90%. A large majority of the detections were within 20 ms of the landmark transcription, and almost all were within 30 ms. The results are analyzed by landmark type and phonetic class.