Human performance in a multisource environment with a frequency-banded minimum-variance beamforming algorithm

Abstract

The effectiveness of a newly developed signal-processing algorithm to extract speech in the presence of multiple interferers was evaluated through intelligibility ratings from normal- and hearing-impaired listeners. Sentences were presented with competing speech and multitalker babble (MTB) at four different SNRs to groups of individuals with sensorineural hearing loss and normal hearing. The sentences and competition had been processed through a real-time, frequency-banded minimum-variance beamforming (FBMVB) algorithm in multitalker environments (Lockwood et al., 1999). Competing messages and MTB were simulated at +22 and +45 azimuths relative to the target source. Each target sentence with competing messages was submitted to the FBMVB algorithm to yield a set of 24 ‘‘processed’’ and 24 ‘‘unprocessed’’ sentences per SNR condition. Listeners rated the intelligibility (0%–100%) of the target sentences and repeated the last word of each sentence. Results from the ratings and the word recognition scores show improvement for every listener when the ensemble speech and competition were processed through the FBMVB algorithm. Results from these experiments indicate the advantage of the algorithm in selective extraction of speech in complex environments. Continued evaluation of the algorithm under real-room conditions is in progress. [Work supported by R21DC04840 ONR N00014-99-1-0612.]