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| Paper: | SP-L8.2 |
| Session: | Acoustic Modeling: New Search Features and Supervised Training |
| Time: | Friday, May 21, 09:50 - 10:10 |
| Presentation: |
Lecture |
| Topic: |
Speech Processing: Acoustic Modeling for Speech Recognition |
| Title: |
COMBINATION OF HIDDEN MARKOV MODELS WITH DYNAMIC TIME WARPING FOR SPEECH RECOGNITION |
| Authors: |
Scott Axelrod; IBM T. J. Watson Research Center | | |
| | Benoît Maison; IBM T. J. Watson Research Center | | |
| Abstract: |
We combine Hidden Markov Models of various topologies and Nearest Neighbor classification techniques in an exponential modeling framework with a model selection algorithm to obtain significant error rate reductions on an isolated word digit recognition task. This work is a preliminary investigation of large scale modeling techniques to be applied to large vocabulary speech recognition. |
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