Prosodically-enhanced Recurrent Neural Network Language Models Siva Reddy Gangireddy and Steve Renals Yoshihiko Nankaku and Akinobu Lee Centre for Speech Technology Research

Source URL: homepages.inf.ed.ac.uk

• Computational linguistics
• Phonology
• Phonetics
• Systemic functional linguistics
• Speech recognition
• Prosody
• Language model
• Acoustic model
• Artificial neural network
• Recurrent neural network
• Intonation
• Syllable

Characteristics of an Effective Simulator under consideration of DirectiveEC Note: In the categories “Visual System”, “Motion System”, “Acoustic System”, “Driver‘s Workplace”, “Vehicle (Calcu

Source URL: www.dvr.de

• Road transport
• Operations research
• Simulation
• Traffic congestion
• Semi-trailer truck
• Vehicle
• Brake
• Driving simulator

Adaptive acoustic species classification in the field Davide Zilli1,2, Lucas Joppa2, Geoff Merrett1 and Alex Rogers1 Agents, Interaction, and Complexity Research Group School of Electronics and Computer Science Universit

Source URL: www.orchid.ac.uk

Seminar Institute for Plasma Research Title : Geodesic acoustic modes Speaker : Rameswar Singh, and O D Gurcan Laboratoire de Physique des Plasmas, Ecole

Source URL: www.ipr.res.in

2009 Fifth IEEE International Conference on e-Science The SweDat Project and Swedia Database for Phonetic and Acoustic Research Jonas Lindh and Anders Eriksson Department of Philosophy, Linguistics and Theory of Science

Source URL: mistral.univ-avignon.fr

Structured Output Layer with Auxiliary Targets for Context-Dependent Acoustic Modelling Pawel Swietojanski, Peter Bell, Steve Renals The Centre for Speech Technology Research, University of Edinburgh, United Kingdom {p.s

Source URL: www.cstr.ed.ac.uk

• Artificial neural networks
• Machine learning
• Computational neuroscience
• Applied mathematics
• Artificial intelligence
• Computational statistics
• Automatic identification and data capture
• Computer accessibility
• Speech recognition
• Language model
• Convolutional neural network
• Perceptron

Responses to reviewers’ comments on “Research design to estimate vaquita abundance” by Rojas-Bracho et al. The figure below summarizes the revised visual and acoustic sampling design for the 2015 vaquita abundance

Source URL: swfsc.noaa.gov

REGULARIZATION OF CONTEXT-DEPENDENT DEEP NEURAL NETWORKS WITH CONTEXT-INDEPENDENT MULTI-TASK TRAINING Peter Bell and Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB, UK ABST

Source URL: www.cstr.ed.ac.uk

• Artificial neural networks
• Machine learning
• Artificial intelligence
• Applied mathematics
• Speech recognition
• Boltzmann machine
• Recurrent neural network
• Multi-task learning
• Deep belief network
• Backpropagation
• Acoustic model
• Language model

Analysis of CNN-based Speech Recognition System using Raw Speech as Input Dimitri Palaz1,2 , Mathew Magimai.-Doss1 , Ronan Collobert3,1 Idiap Research Institute, Martigny, Switzerland Ecole Polytechnique F´ed´erale de

Source URL: ronan.collobert.com

• Artificial neural networks
• Computational neuroscience
• Computational linguistics
• Applied mathematics
• Artificial intelligence
• Mathematics
• Signal processing
• Deep learning
• Convolutional neural network
• Speech recognition
• Acoustic model
• Mel-frequency cepstrum

Complementary tasks for context-dependent deep neural network acoustic models Peter Bell, Steve Renals Centre for Speech Technology Research, University of Edinburgh, Edinburgh EH8 9AB, UK {peter.bell, s.renals}@ed.ac.uk

Source URL: www.cstr.ed.ac.uk

• Artificial intelligence
• Machine learning
• Learning
• Applied mathematics
• Computational linguistics
• Automatic identification and data capture
• Computer accessibility
• Speech recognition
• Artificial neural network
• Multi-task learning
• Recurrent neural network
• Statistical classification