Abstract
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.
Original language | English |
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Pages (from-to) | 1403-1406 |
Number of pages | 4 |
Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
Publication status | Published - 2009 Nov 26 |
Externally published | Yes |
Event | 10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom Duration: 2009 Sep 6 → 2009 Sep 10 |
Keywords
- Decision trees
- Hidden Markov model (HMM)
- Speech recognition
ASJC Scopus subject areas
- Human-Computer Interaction
- Signal Processing
- Software
- Sensory Systems