Text-dependent speaker identification based on input/output HMMs: an empirical study
Neural Processing Letters
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
A modified HME architecture for text-dependent speaker identification
IEEE Transactions on Neural Networks
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Input/output hidden Markov model (IOHMM) has turned out to be effective in sequential data processing via supervised learning. However, there are several difficulties, e.g. model selection, unexpected local optima and high computational complexity, which hinder an IOHMM from yielding the satisfactory performance in sequence classification. Unlike previous efforts, this paper presents an ensemble learning approach to tackle the aforementioned problems of the IOHMM. As a result, simple IOHMMs of different topological structures are used as base learners in our boosting algorithm and thus an ensemble of simple IOHMMs tend to tackle a complicated sequence classification problem without the need of explicit model selection. Simulation results in text-dependent speaker identification demonstrate the effectiveness of boosted IOHMMs for sequence classification.