In the framework of the European HearCom project, promising signal enhancement algorithms were developed and evaluated for future use in hearing instruments. To assess the algorithms' performance, five of the algorithms were selected and implemented on a common real-time hardware/software platform. Four test centers in Belgium, The Netherlands, Germany, and Switzerland perceptually evaluated the algorithms. Listening tests were performed with large numbers of normal-hearing and hearing-impaired subjects. Three perceptual measures were used: speech reception threshold (SRT), listening effort scaling, and preference rating. Tests were carried out in two types of rooms. Speech was presented in multitalker babble arriving from one or three loudspeakers. In a pseudo-diffuse noise scenario, only one algorithm, the spatially preprocessed speech-distortion-weighted multi-channel Wiener filtering, provided a SRT improvement relative to the unprocessed condition. Despite the general lack of improvement in SRT, some algorithms were preferred over the unprocessed condition at all tested signal-to-noise ratios (SNRs). These effects were found across different subject groups and test sites. The listening effort scores were less consistent over test sites. For the algorithms that did not affect speech intelligibility, a reduction in listening effort was observed at 0 dB SNR.
An increased listing effort represents a major problem in humans with hearing impairment. Neurodiagnostic methods for an objective listening effort estimation might support hearing instrument fitting procedures. However the cognitive neurodynamics of listening effort is far from being understood and its neural correlates have not been identified yet. In this paper we analyze the cognitive neurodynamics of listening effort by using methods of forward neurophysical modeling and time-scale electroencephalographic neurodiagnostics. In particular, we present a forward neurophysical model for auditory late responses (ALRs) as large-scale listening effort correlates. Here endogenously driven top-down projections related to listening effort are mapped to corticothalamic feedback pathways which were analyzed for the selective attention neurodynamics before. We show that this model represents well the time-scale phase stability analysis of experimental electroencephalographic data from auditory discrimination paradigms. It is concluded that the proposed neurophysical and neuropsychological framework is appropriate for the analysis of listening effort and might help to develop objective electroencephalographic methods for its estimation in future.
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