Current hearing aids are limited with respect to speech-specific optimization for spatial sound sources to perform speech enhancement. In this study, we therefore propose an approach for spatial detection of speech based on sound source localization and blind optimization of speech enhancement for binaural hearing aids. We have combined an estimator for the direction of arrival (DOA), featuring high spatial resolution but no specialization to speech, with a measure of speech quality with low spatial resolution obtained after directional filtering. The DOA estimator provides spatial sound source probability in the frontal horizontal plane. The measure of speech quality is based on phoneme representations obtained from a deep neural network, which is part of a hybrid automatic speech recognition (ASR) system. Three ASR-based speech quality measures (ASQM) are explored: entropy, mean temporal distance (M-Measure), matched phoneme (MaP) filtering. We tested the approach in four acoustic scenes with one speaker and either a localized or a diffuse noise source at various signal-to-noise ratios (SNR) in anechoic or reverberant conditions. The effects of incorrect spatial filtering and noise were analyzed. We show that two of the three ASQMs (M-Measure, MaP filtering) are suited to reliably identify the speech target in different conditions. The system is not adapted to the environment and does not require a-priori information about the acoustic scene or a reference signal to estimate the quality of the enhanced speech signal. Nevertheless, our approach performs well in all acoustic scenes tested and varying SNRs and reliably detects incorrect spatial filtering angles.

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models.