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The value of auditory offset adaptation and appropriate acoustic modeling

A critical step in encoding sound for neuronal processing occurs when the analog pressure wave is coded into discrete nerve-action potentials. Recent pool models of the inner hair cell synapse do not reproduce the dead time period after an intense stimulus, so we used visual inspection and automatic speech recognition (ASR) to investigate an offset adaptation (OA) model proposed by Zhang et al. [1]. OA improved phase locking in the auditory nerve (AN) and raised ASR accuracy for features derived from AN fibers (ANFs). We also found that OA is crucial for auditory processing by onset neurons (ONs) in the next neuronal stage, the auditory brainstem. Multi-layer perceptrons (MLPs) performed much better than standard Gaussian mixture models (GMMs) for both our ANF-based and ON-based auditory features. Similar results were previously obtained with MSG (Modulation-filtered SpectroGram) auditory features[2]. Thus we believe researchers working with novel features should consider trying MLPs.