10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Decision Tree Acoustic Models for ASR

Jitendra Ajmera, Masami Akamine

Toshiba Corporate R&D Center, Japan

This paper presents a summary of our research progress using decision-tree acoustic models (DTAM) for large vocabulary speech recognition. Various configurations of training DTAMs are proposed and evaluated on wall-street journal (WSJ) task. A number of different acoustic and categorical features have been used for this purpose. Various ways of realizing a forest instead of a single tree have been presented and shown to improve recognition accuracy. Although the performance is not shown to be better than Gaussian mixture models (GMMs), several advantages of DTAMs have been highlighted and exploited. These include compactness, computational simplicity and ability to handle unordered information.

Full Paper

Bibliographic reference.  Ajmera, Jitendra / Akamine, Masami (2009): "Decision tree acoustic models for ASR", In INTERSPEECH-2009, 1403-1406.