TY - CHAP U1 - Konferenzveröffentlichung A1 - Macek, Jan A1 - Kanokphara, Supphanat A1 - Geumann, Anja T1 - Articulatory-acoustic Feature Recognition: Comparison of Machine Learning and HMM methods T2 - Proceedings of the 10th International Conference on Speech and Computer, 17-19 October, Patras, Greece (SPECOM 2005) N2 - 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. KW - Automatische Spracherkennung KW - Artikulatorische Phonetik Y1 - 2005 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-57041 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-57041 SN - 9785745201103 SB - 9785745201103 SP - 99 EP - 102 PB - University of Patras CY - Patras ER -