MLASP 2014 - GlobalSIP Symposium: Machine Learning Applications in Speech Processing

Recent advances in machine learning, signal and information processing have brought about new opportunities to solve several long-standing technological challenges in acoustic speech processing. This symposium focuses on the use of such advances to improve the state of the art in hands-free, human-to-human and human-to-machine speech applications. It is well known that speech signals captured by distant microphones in real-world environments are generally corrupted by reverberation and interference from competing audio sources. These acoustic distortions pose a significant hinderance to the wider adoption of voice-driven applications. The objective of this symposium is to foster new signal and information processing trends in the field of speech by creating a platform that brings together researchers and practitioners from academia and industry to propose and exchange ideas and findings on this topic.
We invite papers describing various aspects of machine learning, signal and information processing for both speech enhancement and recognition. Topics of interest in this symposium include:
Machine learning for audio and acoustic signal processing
Speech enhancement
Statistical microphone array processing and beamforming
Automatic speech recognition
Spoken language processing
Keynote Speakers
Organizers
Chairs
John R. Hershey
Paris Smaragdis
TPC Members:
Shoji Makino
Tomohiro Nakatani
George Saon
Michael Seltzer
Mehrez Souden
Shinji Watanabe
Contacts
For all inquiries and questions please contact Dr. Mehrez Souden at msouden6-AT-ece.gatech.edu