Abstract

Auditory attention is a complex mechanism that involves the processing of low-level acoustic cues together with higher level cognitive cues. In this paper, a novel method is proposed that combines biologically inspired auditory attention cues with higher level lexical and syntactic information to model task-dependent influences on a given spoken language processing task. A set of low-level multiscale features (intensity, frequency contrast, temporal contrast, orientation, and pitch) is extracted in parallel from the auditory spectrum of the sound based on the processing stages in the central auditory system to create feature maps that are converted to auditory gist features that capture the essence of a sound scene. The auditory attention model biases the gist features in a task-dependent way to maximize target detection in a given scene. Furthermore, the top-down task-dependent influence of lexical and syntactic …

Date

June 5, 2009

Authors

Ozlem Kalinli, Shrikanth Narayanan

Journal

IEEE Transactions on audio, Speech, and language processing

Volume

17

Issue

5

Pages

1009-1024

Publisher

IEEE