Auditory nerve fiber models provide further insight into the inner workings of the
ear and brain. These models have helped us to develop physiologically based speech
intelligibility metrics, to assess the difficulty of understanding speech objectively.
Several metrics have been developed, but they have been developed using a range
of auditory nerve (AN) fiber models. A full comparison of different metrics on even
footing should be performed to evaluate the accuracy of their predictions.
Speech intelligibility is understood to be dependant on both temporal fine structure
and envelope cues in the acoustic speech signal, which are however linked in a way
where they are very difficult to split. This makes the evaluation of speech intelligibility
metrics tricky, as metrics often aim to analyze mean rate and fine timing information
in the auditory nerve representation of the acoustic cues.
In this study, a method of phase distortion was developed, with the goal of degrading
the fine timing information of a speech signal to the point where only the mean rate
representation in the AN is contributing to the speech intelligibility. Also, the neural
cross correlation coefficients developed in Heinz & Swaminathan (2009) were adapted
from the Zilany & Bruce (2007) auditory nerve model to the Bruce, Erfani & Zilany
(2018) AN model. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26120 |
| Date | January 2020 |
| Creators | Clarke, Sean |
| Contributors | Bruce, Ian, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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