| 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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