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Articulatory-acoustic Feature Recognition: Comparison of Machine Learning and HMM methods
| Citations: | 2 - 1 self |
BibTeX
@MISC{Macek_articulatory-acousticfeature,
author = {Jan Macek and Anja Geumann},
title = {Articulatory-acoustic Feature Recognition: Comparison of Machine Learning and HMM methods},
year = {}
}
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Abstract
HMMs are the dominating technique used in speech recognition today since they perform well in overall phone recognition. In this paper, we show the comparison of HMM methods and machine learning techniques, such as neural networks, decision trees and ensemble classifiers with boosting and bagging in the task of articulatory-acoustic feature classification. The experimental results show that HMM methods work well for the classification of such features as vocalic. However, decision tree and bagging outperform HMMs for the fricative classification task since the data skewness is much higher than for the feature vocalic classification task. This demonstrates that HMMs do not perform as well as decision trees and bagging in highly skewed data settings. 1.
Keyphrases
hmm method decision tree machine learning articulatory-acoustic feature recognition data setting articulatory-acoustic feature classification speech recognition today feature vocalic classification task overall phone recognition fricative classification task outperform hmms machine learning technique ensemble classifier experimental result dominating technique neural network data skewness
