Abstract

A top-down task-dependent model guides attention to likely target locations in cluttered scenes. Here, a novel biologically plausible top-down auditory attention model is presented to model such task-dependent influences on a given task. First, multi-scale features are extracted based on the processing stages in the central auditory system, and converted to low-level auditory "gist" features. These features capture rough information about the overall scene. Then, the top-down model learns the mapping between auditory gist features and the scene categories. The proposed top-down attention model is tested with prominent syllable detection task in speech. When tested on broadcast news-style read speech using the BU Radio News Corpus, the model achieves 85.8% prominence detection accuracy at syllable level. The results compare well to the reported human performance on this task.

Date

March 31, 2008

Authors

Ozlem Kalinli, Shrikanth Narayanan

Conference

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

Pages

3981-3984

Publisher

IEEE