Diphone subspace mixture trajectory models for HMM Complementation
Speech Communication
Human Speech Perception: Some Lessons from Automatic Speech Recognition
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
IEICE - Transactions on Information and Systems
IEEE Transactions on Audio, Speech, and Language Processing
Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)
International Journal of Speech Technology
International Journal of Speech Technology
Long-Term temporal features for conversational speech recognition
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
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We study a new approach to processing temporal information for automatic speech recognition (ASR). Specifically, we study the use of rather long-time temporal patterns (TRAPs) of spectral energies in place of the conventional spectral patterns for ASR. The proposed neural TRAPs are found to yield significant amount of complementary information to that of the conventional spectral feature based ASR system. A combination of these two ASR systems is shown to result in improved robustness to several types of additive and convolutive environmental degradations.