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SP-P1: Acoustic Modeling: Discriminative methods

Session Type: Poster

Time: Tuesday, May 28, 10:50 - 12:50

Location: Poster Area D

Session Chair: Gernot Kubin, Graz University of Technology

SP-P1.1: EFFECTIVENESS OF DISCRIMINATIVE TRAINING AND FEATURE TRANSFORMATION FOR REVERBERATED AND NOISY SPEECH

Yuuki Tachioka; Mitsubishi Electric

Shinji Watanabe; Mitsubishi Electric Research Laboratories (MERL)

John R. Hershey; Mitsubishi Electric Research Laboratories (MERL)

SP-P1.2: TIED-STATE BASED DISCRIMINATIVE TRAINING OF CONTEXT-EXPANDED REGION-DEPENDENT FEATURE TRANSFORMS FOR LVCSR

Zhi-Jie Yan; Microsoft Research Asia

Qiang Huo; Microsoft Research Asia

Jian Xu; Microsoft Research Asia

Yu Zhang; Microsoft Research Asia

SP-P1.3: STATE OF THE ART DISCRIMINATIVE TRAINING OF SUBSPACE CONSTRAINED GAUSSIAN MIXTURE MODELS IN BIG TRAINING CORPORA

Jing Huang; IBM

Peder Olsen; IBM

Vaibhava Goel; IBM

SP-P1.4: KERNELIZED LOG LINEAR MODELS FOR CONTINUOUS SPEECH RECOGNITION

Shi-Xiong Zhang; Cambridge University

Mark J.F. Gales; Cambridge University

SP-P1.5: A CRITICAL EVALUATION OF STOCHASTIC ALGORITHMS FOR CONVEX OPTIMIZATION

Simon Wiesler; RWTH Aachen University

Alexander Richard; RWTH Aachen University

Ralf Schlüter; RWTH Aachen University

Hermann Ney; RWTH Aachen University

SP-P1.6: ADAPTIVE BOOSTED NON-UNIFORM MCE FOR KEYWORD SPOTTING ON SPONTANEOUS SPEECH

Chao Weng; Georgia Institute of Technology

Biing-Hwang (Fred) Juang; Georgia Institute of Technology

SP-P1.7: MULTI-TASK LEARNING IN DEEP NEURAL NETWORKS FOR IMPROVED PHONEME RECOGNITION

Michael Seltzer; Microsoft Corporation

Jasha Droppo; Microsoft Corporation

SP-P1.8: DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

Zoltán Tüske; RWTH Aachen University

Ralf Schlüter; RWTH Aachen University

Hermann Ney; RWTH Aachen University

SP-P1.9: MULTI-LEVEL ADAPTIVE NETWORKS IN TANDEM AND HYBRID ASR SYSTEMS

Peter Bell; University of Edinburgh

Pawel Swietojanski; University of Edinburgh

Steve Renals; University of Edinburgh

SP-P1.10: INCOHERENT TRAINING OF DEEP NEURAL NETWORKS TO DE-CORRELATE BOTTLENECK FEATURES FOR SPEECH RECOGNITION

Yebo Bao; University of Science and Technology of China

Hui Jiang; York University

Li-Rong Dai; University of Science and Technology of China

Cong Liu; Anhui USTC iFLYTEK Corporation Limited

SP-P1.11: PHONE RECOGNITION WITH DEEP SPARSE RECTIFIER NEURAL NETWORKS

Laszlo Toth; Research Group on Artificial Intelligence

SP-P1.12: WARPED MINIMUM VARIANCE DISTORTIONLESS RESPONSE BASED BOTTLE NECK FEATURES FOR LVCSR

Kevin Kilgour; Karlsruhe Institute of Technology (KIT)

Igor Tseyzer; Karlsruhe Institute of Technology (KIT)

Quoc Bao Nguyen; Karlsruhe Institute of Technology (KIT)

Alex Waibel; Karlsruhe Institute of Technology (KIT)

SP-P1.13: EFFICIENT MANIFOLD LEARNING FOR SPEECH RECOGNITION USING LOCALITY SENSITIVE HASHING

Vikrant Tomar; McGill University

Richard Rose; McGill University

SP-P1.14: TYING ROTATIONS OF COVARIANCE MATRICES VIA RIEMANNIAN SUBSPACE CLUSTERING

Yusuke Shinohara; Toshiba Corporation