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Abstract
Previous studies have shown that concurrent vowel identification improves with increasing temporal onset asynchrony of the vowels, even if the vowels have the same fundamental frequency. The current study investigated the possible underlying neural processing involved in concurrent vowel perception. The individual vowel stimuli from a previously published study were used as inputs for a phenomenological auditory-nerve (AN) model. Spectrotemporal representations of simulated neural excitation patterns were constructed (i.e., neurograms) and then matched quantitatively with the neurograms of the single vowels using the Neurogram Similarity Index Measure (NSIM). A novel computational decision model was used to predict concurrent vowel identification. To facilitate optimum matches between the model predictions and the behavioral human data, internal noise was added at either neurogram generation or neurogram matching using the NSIM procedure. The best fit to the behavioral data was achieved with a signal-to-noise ratio (SNR) of 8 dB for internal noise added at the neurogram but with a much smaller amount of internal noise (SNR of 60 dB) for internal noise added at the level of the NSIM computations. The results suggest that accurate modeling of concurrent vowel data from listeners with normal hearing may partly depend on internal noise and where internal noise is hypothesized to occur during the concurrent vowel identification process.
Highlights
Human listeners often engage in conversation in an acoustic environment where the surrounding voices interfere with understanding the speech produced by the talker of interest
This paper presents a neural computational modeling study of concurrent vowel perception
Previous research has shown that concurrent vowel perception improves with increasing temporal onset asynchrony of the vowels, even if the vowels have the same fundamental frequency
Summary
Human listeners often engage in conversation in an acoustic environment where the surrounding voices interfere with understanding the speech produced by the talker of interest. To understand the potential psychoacoustic and neural mechanisms for speech understanding in background noise, the concurrent vowel identification paradigm has been widely used with reference to competing talkers [3, 4, 5, 6]. Because vowels may be separated by differences in fundamental frequency (F0), models to explain identification of concurrent vowels have frequently focused on F0 segregation. There are, other cues which may be used to separate concurrent vowels One such cue is temporal onsets/offsets [7, 8]. Previous research has shown that temporal asynchronous onsets can be efficiently used by listeners to separate and identify concurrent vowel stimuli [9, 10, 11, 12], even if both vowels have the same F0
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