Psychological and Physiological Acoustics Session 4pPP: Computational Modeling of Sensorineural Hearing Loss: Models and Applications

Joshua M. Alexander* and Varsha Hariram​​*Corresponding author's address: Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, Indiana 47907,alexan14@purdue.edu Neural-Scaled Entropy (NSE) is an objective metric used to quantify 'information' available in speech consequent hearing loss, hearing aidsignal processing, and distortion from various environmental factors. One pursuit is to use NSE to find optimum hearing aid settings thatmaximize speech perception. Inspired by the Cochlear-Scaled Entropy model [Stilp et al., 2010, J. Acoust. Soc. Am., 2112-2126], NSE usesthe neural spike output at the inner hair cell synapse of an auditory nerve model [Zilany et al. 2009, J. Acoust. Soc. Am., 126, 2390-2412].Probability of spike output from fibers sampled at equidistant places along the model cochlea is computed for short duration time frames.Potential information is estimated by using the Kullback-Liebler Divergence to describe how the pattern of neural firing at each frame differsfrom preceding frames in an auto-regressive manner. NSE was tested using nonsense syllables from various perceptual studies that includeddifferent signal processing schemes and was compared to performance for different vowel-defining parameters, consonant features, and talkergender. NSE has potential to serve as a model predictor of speech perception, and to capture the effects of sensorineural hearing loss beyondsimple filter broadening. [Supported by NIDCD RC1DC010601]Published by the Acoustical Society of America through the American Institute of Physics