Boosting a weak learning algorithm by majority
COLT '90 Proceedings of the third annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Performance Analysis and Comparison of Linear Combiners for Classifier Fusion
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Speaker Normalization Based on Frequency Warping
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Speaker normalization on conversational telephone speech
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Linear combiners for classifier fusion: some theoretical and experimental results
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Robust clustering: based realtime vowel recognition
Acta Cybernetica
Machine transliteration survey
ACM Computing Surveys (CSUR)
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In the therapy of the hearing impaired one of the key problems is how to deal with the lack of proper auditive feedback which impedes the development of intelligible speech. The effectiveness of the therapy relies heavily on accurate phoneme recognition [1, 4, 17]. Because of the environmental difficulties, simple recognition algorithms may have a weak classification performance, so various techniques such as normalization and classifier combination are applied to increase the recognition accuracy. This paper examines Vocal Tract Length Normalization techniques [5, 13] focusing mainly on the real-time parameter estimation [12], and the majority of classifier combination schemes, including the traditional (Prod, Sum, Min, Max) [7], basic linear (simple, weighted, AHP-based [6] averaging), and some special linear (Bagging, Boosting) combinations. Based on the results we conclude that hybrid combinations can improve the effectiveness of the real-time normalization methods.