ICSI Research Launches New Speech Recognition Project
The International Computer Science Institute (ICSI) announced a new research project focused on exploring automatic speech recognition (ASR) to understand the limitations and challenges from current technologies.
Sponsored by the Intelligence Advanced Research Projects Activity via the Air Force Research Lab, the research aims to use its conclusions to lead to new methods for improving ASR technology. The one-year project is expected to be completed by March 2013.
"This is a unique research project in that we are qualitatively and quantitatively exploring what is wrong with automatic speech recognition," said Nelson Morgan, leader of the speech research activity at ICSI, in a statement. "From that we hope to gain insights into how we can improve ASR, potentially going forward in entirely new directions. When you don't know specifically what is wrong with a technology, you are left with a hit-or-miss situation. This research should give us some clarity."
The research project includes two major parts. The first is an in-depth look at the assumptions behind acoustic modeling, which is a key component of ASR that creates statistical representations of each of the distinctive sounds that make up words. This will enable ICSI researchers to discover technical challenges that prevent ASR from being more accurate.
The second part is a broad survey of experts and colleagues in the field, asking for perceptions on where ASR technology is effective, where it fails, and what its shortcomings are. This study will include interviews with practitioners and a review of recent literature to derive community consensus on what approaches don't work, and to develop guidelines for future analysis.
Steven Wegmann is serving as coprincipal investigator of the research, overseeing the in-depth acoustic modeling phase. Coprincipal investigator Jordan Cohen is heading the breadth field survey phase. Morgan is the principal investigator for the full research project.
Collaboration will focus on human-machine interaction.