Optimization of binaural algorithms for maximum predicted speech intelligibility

Abstract

At the threshold of understanding speech in noisy environments, the neural exploitation of interaural differences constitutes a significant margin of intelligibility. Mimicking spatial hearing for improving speech intelligibility in hearing aids and robotics has been demonstrated by compelling results. This chapter reviews binaural speech processors and highlights approaches for their optimal application. Binaural algorithms of speech enhancement draw on the assumption that the target and the noise signal strike a head-mounted processor from different directions and cause distinctive interaural parameters. This assumption is, however, often violated in degraded acoustics. In order to study this degradation, the chapter starts with an examination of binaural statistics in different noise conditions. Subsequently, standard binaural speech processors are studied that use different waveform features, namely, the binaural coherence of the fine-structure, the binaural differences of the fine-structure and the binaural differences of the envelope. As a means to cater to a fair comparison, each algorithm underwent a stochastic optimization of the algorithmic parameters in a set of prototypical speech-in-noise scenes, whereby an instrumental measure of speech intelligibility served as the objective function. Furthermore, the binaural speech processors are applied at the output of commercially-available hearing aids that feature superdirective beamformers with different directivity modes. In this way, the SNR-gain that adds to the pre-processing of the beamformer is assessed. For deriving filter gains from binaural statistics, part three of this chapter describes histogram-based methods and parametric approaches for the binaural fine-structure algorithm, and compares these in a realistic environment with reverberation and additive noise. Part three can also be read as a hands-on description, thereby addressing students and engineers, who are striving for an ad-hoc implementation of a binaural system for speech enhancement.