Predicting Speech Intelligibility using the Spike Acativity Mutual Information Index

Abstract

The spike activity mutual information index (SAMII) is presented as a new intrusive objective metric to predict speech intelligibility. A target speech signal and speech-in-noise signal are processed by a state-of-the-art computational model of the peripheral auditory system. It simulates the neural activity in a population of auditory nerve fibers (ANFs), which are grouped into critical bands covering the speech frequency range. The mutual information between the neural activity of both signals is calculated using analysis windows of 20 ms. Then, the mutual information is averaged along these analysis windows to obtain SAMII. SAMII is also extended to binaural scenarios by calculating the index for the left ear, right ear, and both ears, choosing the best case for predicting intelligibility. SAMII was developed based on the first clarity prediction challenge training dataset and compared to the modified binaural short-time objective intelligibility (MBSTOI) as baseline. Scores are reported in root mean squared error (RMSE) between measured and predicted data using the clarity challenge test dataset. SAMII scored 35.16%, slightly better than the MBSTOI which obtained 36.52%. This work leads to the conclu-sion that SAMII is a reliable objective metric when “low-level” representations of the speech, such as spike activity, are used.