Technical Program

SP-L3: Acoustic Modeling with Neural Networks

Session Type: Lecture
Time: Wednesday, May 29, 08:00 - 10:00
Location: Room 211
Session Chair: Brian Kingsbury, IBM
 
SP-L3.1: DEEP NEURAL NETWORK FEATURES AND SEMI-SUPERVISED TRAINING FOR LOW RESOURCE SPEECH RECOGNITION
         Samuel Thomas; The Johns Hopkins University
         Michael Seltzer; Microsoft Research
         Kenneth Church; IBM Research
         Hynek Hermansky; The Johns Hopkins University
 
SP-L3.2: INVESTIGATING DEEP NEURAL NETWORK BASED TRANSFORMS OF ROBUST AUDIO FEATURES FOR LVCSR
         Enrico Bocchieri; AT&T Labs Research
         Dimitrios Dimitriadis; AT&T Labs Research
 
SP-L3.3: FEATURE COMBINATION AND STACKING OF RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR LVCSR
         Christian Plahl; RWTH Aachen University
         Michał Kozielski; RWTH Aachen University
         Ralf Schlüter; RWTH Aachen University
         Hermann Ney; RWTH Aachen University
 
SP-L3.4: MLP-BASED FACTOR ANALYSIS FOR TANDEM SPEECH RECOGNITION
         Marc Ferras; Idiap Research Institute
         Hervé Bourlard; Idiap Research Institute
 
SP-L3.5: AN EMPIRICAL STUDY OF LEARNING RATES IN DEEP NEURAL NETWORKS FOR SPEECH RECOGNITION
         Andrew Senior; Google Inc.
         Georg Heigold; Google Inc.
         Marc'Aurelio Ranzato; Google Inc.
         Ke Yang; Google Inc.
 
SP-L3.6: GAUSSIAN-BERNOULLI RESTRICTED BOLTZMANN MACHINES AND AUTOMATIC FEATURE EXTRACTION FOR NOISE ROBUST MISSING DATA MASK ESTIMATION
         Sami Keronen; Aalto University School of Science
         KyungHyun Cho; Aalto University School of Science
         Tapani Raiko; Aalto University School of Science
         Alexander Ilin; Aalto University School of Science
         Kalle Palomäki; Aalto University School of Science