Cue Integration With Categories: Weighting Acoustic Cues in Speech Using Unsupervised Learning and Distributional Statistics, Cognitive Science, vol.7, issue.3, pp.434-464, 2010. ,
DOI : 10.3758/BF03208224
Harnessing graphics processors for the fast computation of acoustic likelihoods in speech recognition, Computer Speech & Language, vol.23, issue.4, pp.510-526, 2010. ,
DOI : 10.1016/j.csl.2009.03.005
Discriminative classifiers with adaptive kernels for noise robust speech recognition, Computer Speech & Language, vol.24, issue.4, pp.648-662, 2010. ,
DOI : 10.1016/j.csl.2009.09.002
URL : https://hal.archives-ouvertes.fr/hal-00508470
Point process models for event-based speech recognition, Speech Communication, vol.51, issue.12, pp.1155-1168, 2009. ,
DOI : 10.1016/j.specom.2009.05.008
Language Acquisition Meets Language Evolution, Cognitive Science, vol.96, issue.7, pp.1131-1157, 2010. ,
DOI : 10.1511/2008.69.3670
The Logical Problem of Language Acquisition, Cognitive Science, vol.34, pp.971-1016, 2010. ,
Unattended exposure to components of speech sounds yields same benefits as explicit auditory training, Cognition, vol.115, issue.3, pp.435-443, 2010. ,
DOI : 10.1016/j.cognition.2010.03.004
Learning of control in a neural architecture of grounded language processing, Cognitive Systems Research, vol.11, issue.1, pp.93-107, 2010. ,
DOI : 10.1016/j.cogsys.2008.08.007
Language acquisition and language change, Wiley Interdisciplinary Reviews: Cognitive Science, vol.5, issue.5, pp.677-684, 2010. ,
DOI : 10.1075/livy.5.07van
Speech perception and production, Wiley Interdisciplinary Reviews: Cognitive Science, vol.29, issue.1, pp.629-647, 2010. ,
DOI : 10.1121/1.1908635
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3740754
Improved voice activity detection algorithm using wavelet and support vector machine, Computer Speech & Language, vol.24, issue.3, pp.531-543, 2010. ,
DOI : 10.1016/j.csl.2009.06.002
Voice activity detection based on statistical models and machine learning approaches, Computer Speech & Language, vol.24, issue.3, pp.515-530, 2010. ,
DOI : 10.1016/j.csl.2009.02.003
Voice activity detection based on using wavelet packet, Digital Signal Processing, vol.20, issue.4, pp.1102-1115, 2010. ,
DOI : 10.1016/j.dsp.2009.11.008
Discriminative training of HMMs for automatic speech recognition: A survey, Computer Speech & Language, vol.24, issue.4, pp.589-608, 2010. ,
DOI : 10.1016/j.csl.2009.08.002
Speech recognition with artificial neural networks, Digital Signal Processing, vol.20, issue.3, pp.763-768, 2010. ,
DOI : 10.1016/j.dsp.2009.10.004
Sentence recognition using artificial neural networks, Knowledge-Based Systems, pp.629-635, 2010. ,
DOI : 10.1016/j.knosys.2008.03.053
Active learning and semi-supervised learning for speech recognition: A unified framework using the global entropy reduction maximization criterion, Computer Speech & Language, vol.24, issue.3, pp.433-444, 2010. ,
DOI : 10.1016/j.csl.2009.03.004
Spoken language understanding using weakly supervised learning, Computer Speech & Language, vol.24, issue.2, pp.358-382, 2010. ,
DOI : 10.1016/j.csl.2009.05.002
A study on integrating acoustic-phonetic information into lattice rescoring for automatic speech recognition, Speech Communication, vol.51, issue.11 ,
DOI : 10.1016/j.specom.2009.05.004
Joint evaluation of multiple speech patterns for speech recognition and training, Computer Speech & Language, vol.24, issue.2, pp.307-340, 2010. ,
DOI : 10.1016/j.csl.2009.05.001
Joint acoustic and language modeling for speech recognition, Speech Communication, vol.52, issue.3, pp.223-235, 2009. ,
DOI : 10.1016/j.specom.2009.10.003
Robust speech recognition by integrating speech separation and hypothesis testing, Speech Communication, vol.52, issue.1, pp.72-81, 2010. ,
DOI : 10.1016/j.specom.2009.08.008
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.5076