Ensemble Methods for Phoneme Classification
Abstract
This paper investigates a number of ensemble methods for improv(cid:173) ing the performance of phoneme classification for use in a speech recognition system. Two ensemble methods are described; boosting and mixtures of experts, both in isolation and in combination. Re(cid:173) sults are presented on two speech recognition databases: an isolated word database and a large vocabulary continuous speech database. These results show that principled ensemble methods such as boost(cid:173) ing and mixtures provide superior performance to more naive en(cid:173) semble methods such as averaging.
Cite
Text
Waterhouse and Cook. "Ensemble Methods for Phoneme Classification." Neural Information Processing Systems, 1996.Markdown
[Waterhouse and Cook. "Ensemble Methods for Phoneme Classification." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/waterhouse1996neurips-ensemble/)BibTeX
@inproceedings{waterhouse1996neurips-ensemble,
title = {{Ensemble Methods for Phoneme Classification}},
author = {Waterhouse, Steve R. and Cook, Gary},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {800-806},
url = {https://mlanthology.org/neurips/1996/waterhouse1996neurips-ensemble/}
}