Phonetic Classification Using Hierarchical, Feed-forward, Spectro-temporal Patch-based Architectures

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[1]  James R. Glass,et al.  Noise Robust Phonetic Classificationwith Linear Regularized Least Squares and Second-Order Features , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[2]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Martin Heckmann,et al.  A hierarchical model for syllable recognition , 2007, ESANN.

[4]  Tomaso Poggio,et al.  Learning a dictionary of shape-components in visual cortex: comparison with neurons, humans and machines , 2006 .

[5]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[6]  Yali Amit,et al.  Robust acoustic object detection. , 2005, The Journal of the Acoustical Society of America.

[7]  N. Morgan,et al.  Pushing the envelope - aside [speech recognition] , 2005, IEEE Signal Processing Magazine.

[8]  Powen Ru,et al.  Multiresolution spectrotemporal analysis of complex sounds. , 2005, The Journal of the Acoustical Society of America.

[9]  Thomas Serre,et al.  Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Jing Peng,et al.  SVM vs regularized least squares classification , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[11]  Paul Lamere,et al.  Sphinx-4: a flexible open source framework for speech recognition , 2004 .

[12]  Daniel P. W. Ellis,et al.  LP-TRAP: linear predictive temporal patterns , 2004, INTERSPEECH.

[13]  Hynek Hermansky.  TRAP-TANDEM: data-driven extraction of temporal features from speech , 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).

[14]  Daniel P. W. Ellis,et al.  Frequency-domain linear prediction for temporal features , 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).

[15]  Christoph E Schreiner,et al.  Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex. , 2003, Journal of neurophysiology.

[16]  Michael Kleinschmidt.  Localized spectro-temporal features for automatic speech recognition , 2003, INTERSPEECH.

[17]  James R. Glass.  A probabilistic framework for segment-based speech recognition , 2003, Comput. Speech Lang..

[18]  David Gelbart,et al.  Improving word accuracy with Gabor feature extraction , 2002, INTERSPEECH.

[19]  K. Sen,et al.  Feature analysis of natural sounds in the songbird auditory forebrain. , 2001, Journal of neurophysiology.

[20]  Albert S. Bregman,et al.  Auditory Scene Analysis , 2001 .

[21]  K. Sen,et al.  Spectral-temporal Receptive Fields of Nonlinear Auditory Neurons Obtained Using Natural Sounds , 2022 .

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[24]  Hervé Bourlard,et al.  The full combination sub-bands approach to noise robust HMM/ANN based ASR , 1999, EUROSPEECH.

[25]  Hynek Hermansky,et al.  Temporal patterns (TRAPs) in ASR of noisy speech , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[26]  Treebank Penn,et al.  Linguistic Data Consortium , 1999 .

[27]  Andrew K. Halberstadt.  Heterogeneous acoustic measurements and multiple classifiers for speech recognition , 1999 .

[28]  James R. Glass,et al.  Heterogeneous measurements and multiple classifiers for speech recognition , 1998, ICSLP.

[29]  Steven Greenberg,et al.  Robust speech recognition using the modulation spectrogram , 1998, Speech Commun..

[30]  M. Merzenich,et al.  Optimizing sound features for cortical neurons. , 1998, Science.

[31]  Richard Lippmann,et al.  Speech recognition by machines and humans , 1997, Speech Commun..

[32]  Hervé Bourlard,et al.  Subband-based speech recognition , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[33]  Steven Greenberg,et al.  The modulation spectrogram: in pursuit of an invariant representation of speech , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[34]  James R. Glass,et al.  A probabilistic framework for feature-based speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[35]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Jont B. Allen,et al.  How do humans process and recognize speech? , 1993, IEEE Trans. Speech Audio Process..

[37]  Jonathan G. Fiscus,et al.  Darpa Timit Acoustic-Phonetic Continuous Speech Corpus CD-ROM {TIMIT} | NIST , 1993 .

[38]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[39]  R. Patterson,et al.  Complex Sounds and Auditory Images , 1992 .

[40]  L. Galway.  Spline Models for Observational Data , 1991 .

[41]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[42]  V. Rich.  Personal communication , 1989, Nature.

[43]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[44]  Sadaoki Furui,et al.  Speaker-independent isolated word recognition using dynamic features of speech spectrum , 1986, IEEE Trans. Acoust. Speech Signal Process..

[45]  M. Cynader,et al.  Sensitivity of cat primary auditory cortex (Al) neurons to the direction and rate of frequency modulation , 1985, Brain Research.

[46]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[47]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[48]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.