Individual factors in speech recognition with binaural multi-microphone noise reduction: Measurement and prediction
Abstract
Multi-microphone noise reduction algorithms give typically rise to large signal-to-noise ratio improvements, but they can also severely distort binaural information and thus compromise spatial hearing abilities. To address this problem Klasen et al. (2007) proposed an extension of the binaural multi-channel Wiener filter (MWF), which suppresses only part of the noise and, in this way, preserves some binaural information (MWF-N). The current study had three aims: (1) to assess aided speech recognition with MWF(-N) for a group of elderly hearing-impaired listeners, (2) to explore the impact of individual factors on their performance, and (3) to test if outcome can be predicted using a binaural speech intelligibility model. Sixteen hearing aid users took part in the study. Speech recognition was assessed using headphone simulations of a spatially complex speech-in-noise scenario. Individual factors were assessed using audiometric, psychoacoustic (binaural), and cognitive measures. Analyses showed clear benefits from both MWF and MWF-N and also suggested sensory and binaural influences on speech recognition. Model predictions were reasonably accurate for MWF but not MWF-N, suggesting a need for some model refinement concerning supra-threshold processing.
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