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Hearing Is Believing: Biologically Inspired Methods for Robust Automatic Speech Recognition.

, and . IEEE Signal Process. Mag., 29 (6): 34-43 (2012)

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Long-Term Temporal Features for Conversational Speech Recognition., , and . MLMI, volume 3361 of Lecture Notes in Computer Science, page 232-242. Springer, (2004)Features Based on Auditory Physiology and Perception., and . Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, (2012)Tonotopic Multi-Layered Perceptron: A Neural Network for Learning Long-Term Temporal Features for Speech Recognition., , and . ICASSP (1), page 945-948. IEEE, (2005)Context-dependent connectionist probability estimation in a hybrid hidden Markov model-neural net speech recognition system., , , , and . Comput. Speech Lang., 8 (3): 211-222 (1994)Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech Recognition., , and . NATO Neurocomputing, volume 68 of NATO ASI Series, page 217-226. Springer, (1989)Recent innovations in speech-to-text transcription at SRI-ICSI-UW., , , , , , , , , and 8 other author(s). IEEE Trans. Speech Audio Process., 14 (5): 1729-1744 (2006)Generalization and Parameter Estimation in Feedforward Nets: Some Experiments, and . TR-89-017. International Computer Science Institute, Berkeley, CA, (1989)On using MLP features in LVCSR., , , and . INTERSPEECH, page 921-924. ISCA, (2004)CDNN: a context dependent neural network for continuous speech recognition., , , and . ICASSP, page 349-352. IEEE Computer Society, (1992)Connectionist Approaches to the Use of Markov Models for Speech Recognition., , and . NIPS, page 213-219. Morgan Kaufmann, (1990)