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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups

, , , , , , , , , , and . IEEE Signal Processing Magazine, 29 (6): 82--97 (2012)

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Broad phonetic class recognition in a Hidden Markov model framework using extended Baum-Welch transformations., , and . ASRU, page 306-311. IEEE, (2007)Scalable Minimum Bayes Risk Training of Deep Neural Network Acoustic Models Using Distributed Hessian-free Optimization., , and . INTERSPEECH, page 10-13. ISCA, (2012)Audio classification using extended baum-welch transformations., , and . INTERSPEECH, page 2969-2972. ISCA, (2007)Enhancing Exemplar-Based Posteriors for Speech Recognition Tasks., , , and . INTERSPEECH, page 2130-2133. ISCA, (2012)A-Functions: A generalization of Extended Baum-Welch transformations to convex optimization., , , , and . ICASSP, page 5164-5167. IEEE, (2011)A generalized family of parameter estimation techniques., , and . ICASSP, page 1725-1728. IEEE, (2009)N-best entropy based data selection for acoustic modeling., , , , and . ICASSP, page 4133-4136. IEEE, (2012)A Sinusoidal Model Approach to Acoustic Landmark Detection and Segmentation for Robust Segment-Based Speech Recognition., and . ICASSP (1), page 525-528. IEEE, (2006)Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks., , , and . ICASSP, page 4580-4584. IEEE, (2015)Speaker location and microphone spacing invariant acoustic modeling from raw multichannel waveforms., , , , , and . ASRU, page 30-36. IEEE, (2015)