Modeling Outcomes with Frequency
Lowering using Neural-Scaled Entropy
Varsha Hariram
Committee:
Joshua Alexander, Ph.D. (Chair)
Michael Heinz, Ph.D.
Keith Kluender, Ph.D.
Signal
processing schemes such as nonlinear frequency compression (NFC) recode speech
information by moving high-frequency information to lower frequency
regions. Perceptual studies have shown
that depending on the dominant speech sound, where compression occurs and the amount
of compression can have a significant effect on perception. Very little is understood about how
frequency-lowered information is coded by the auditory periphery or even what
constitutes “information.” We have developed
a measure that is sensitive to information in the altered speech signal in an
attempt to predict optimal frequency lowering settings for individual hearing
losses. The Neural-Scaled Entropy (NSE)
model examines the effects of frequency-lowered speech at the level of the
inner hair cell synapse of the auditory nerve model [Zilany et al. 2009, J. Acoust. Soc. Am., 126, 2390-2412]. NSE quantifies the information available in
speech by the degree to which the pattern of neural firing across frequency
changes relative to its past history (entropy). Nonsense syllables with different NFC
parameters were processed through a variety simulated hearing losses using the
auditory nerve model. Results are
compared to perceptual data across the NFC parameters as well as across
different vowel-defining parameters, consonant features, and talker gender.