Prediction of speech intelligibility using pseudo-binaural room impulse responses.

Head orientation (HO) affects better-ear-listening and spatial-release-from-masking, which are two key aspects in binaural speech intelligibility. To incorporate HO in speech intelligibility prediction, binaural room impulse responses (BRIRs) for every HO of interest could be used. Due to the limited spectral bandwidth of speech, however, approximate representations might be sufficient, which can be measured more quickly. A comparison was done between pseudo-BRIRs generated with a motion tracked binaural microphone array and a first order Ambisonics microphone using the spatial decomposition method (SDM). The accuracy of the Ambisonics/SDM approach was comparable to that of real BRIRs, indicating its suitability for speech intelligibility prediction.

Segmentation of binaural room impulse responses for speech intelligibility prediction.

The two most important aspects in binaural speech perception-better-ear-listening and spatial-release-from-masking-can be predicted well with current binaural modeling frameworks operating on head-related impulse responses, i.e., anechoic binaural signals. To incorporate effects of reverberation, a model extension was proposed, splitting binaural room impulse responses into an early, useful, and late, detrimental part, before being fed into the modeling framework. More recently, an interaction between the applied splitting time, room properties, and the resulting prediction accuracy was observed. This interaction was investigated here by measuring speech reception thresholds (SRTs) in quiet with 18 normal-hearing subjects for four simulated rooms with different reverberation times and a constant room geometry. The mean error with one of the most promising binaural prediction models could be reduced by about 1 dB by adapting the applied splitting time to room acoustic parameters. This improvement in prediction accuracy can make up a difference of 17% in absolute intelligibility within the applied SRT measurement paradigm.