Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Model Parameter Estimation for Mixture Density Polynomial Segment Models
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Heterogeneous acoustic measurements and multiple classifiers for speech recognition
Heterogeneous acoustic measurements and multiple classifiers for speech recognition
Neural Computation
Fast Independent Component Analysis in Kernel Feature Spaces
SOFSEM '01 Proceedings of the 28th Conference on Current Trends in Theory and Practice of Informatics Piestany: Theory and Practice of Informatics
Application of Feature Transformation and Learning Methods in Phoneme Classification
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
A Nonlinearized Discriminant Analysis and Its Application to Speech Impediment Therapy
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
Improving the multi-stack decoding algorithm in a segment-based speech recognizer
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
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This paper presents a stochastic segmental speech recogniser that models the a posteriori probabilities directly. The main issues concerning the system are segmental phoneme classification, utterance-level aggregation and the pruning of the search space. For phoneme classification, artificial neural networks and support vector machines are applied. Phonemic segmentation and utterance-level aggregation is performed with the aid of anti-phoneme modelling. At the phoneme level, the system convincingly outperforms the HMM system trained on the same corpus, while at the word level it attains the performance of the HMM system trained without embedded training.