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Dynamic selection of acoustic features in an Automatic Speech Recognition system.

, , , , and . EUSIPCO, page 1-4. IEEE, (2006)

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A study on the effect of adding new dimensions to trajectories in the acoustic space., , , and . EUROSPEECH, page 1503-1506. ISCA, (1999)Multi-Source Neural Networks for Speech Recognition: A Review of Recent Results., , , and . IJCNN (5), page 265-270. IEEE Computer Society, (2000)0-7695-0619-4.Multiple resolution analysis for robust automatic speech recognition., , , and . Comput. Speech Lang., 20 (1): 2-21 (2006)Speeding up MLP Execution through Difference Forward Propagation., , and . Neural Processing Letters, 8 (2): 163-179 (1998)Hybrid HMM-NN modeling of stationary-transitional units for continuous speech recognition., , and . Inf. Sci., 123 (1-2): 3-11 (2000)Controlling Inductive Search in RIGEL Learning System., and . IPMU, volume 313 of Lecture Notes in Computer Science, page 171-178. Springer, (1988)Variability of automatic speech recognition systems using different features., , , , and . INTERSPEECH, page 221-224. ISCA, (2005)Experiments on hiwire database using denoising and adaptation with a hybrid HMM-ANN model., , and . INTERSPEECH, page 2429-2432. ISCA, (2007)Semi-Supervised Learning with Data Augmentation for End-to-End ASR., , , , and . INTERSPEECH, page 2802-2806. ISCA, (2020)Robust multiple resolution analysis for automatic speech recognition., , , and . INTERSPEECH, page 3033-3036. ISCA, (2003)