Predictions of formant-frequency discrimination in noise based on model auditory-nerve responses

Effects of sensorineural hearing loss on formant-frequency discrimination: Measurements and models

This study concerns the effect of hearing loss on discrimination of formant frequencies in vowels. In the response of the healthy ear to a harmonic sound, auditory-nerve (AN) rate functions fluctuate at the fundamental frequency, F0. Responses of inner-hair-cells (IHCs) tuned near spectral peaks are captured (or dominated) by a single harmonic, resulting in lower fluctuation depths than responses of IHCs tuned between spectral peaks. Therefore, the depth of neural fluctuations (NFs) varies along the tonotopic axis and encodes spectral peaks, including formant frequencies of vowels. This NF code is robust across a wide range of sound levels and in background noise. The NF profile is converted into a rate-place representation in the auditory midbrain, wherein neurons are sensitive to low-frequency fluctuations. The NF code is vulnerable to sensorineural hearing loss (SNHL) because capture depends upon saturation of IHCs, and thus the interaction of cochlear gain with IHC transduction. In this study, formant-frequency discrimination limens (DLFFs) were estimated for listeners with normal hearing or mild to moderate SNHL. The F0 was fixed at 100 Hz, and formant peaks were either aligned with harmonic frequencies or placed between harmonics. Formant peak frequencies were 600 and 2000 Hz, in the range of first and second formants of several vowels. The difficulty of the task was varied by changing formant bandwidth to modulate the contrast in the NF profile. Results were compared to predictions from model auditory-nerve and inferior colliculus (IC) neurons, with listeners' audiograms used to individualize the AN model. Correlations between DLFFs, audiometric thresholds near the formant frequencies, age, and scores on the Quick speech-in-noise test are reported. SNHL had a strong effect on DLFF for the second formant frequency (F2), but relatively small effect on DLFF for the first formant (F1). The IC model appropriately predicted substantial threshold elevations for changes in F2 as a function of SNHL and little effect of SNHL on thresholds for changes in F1.

Human discrimination and modeling of high-frequency complex tones shed light on the neural codes for pitch

Accurate pitch perception of harmonic complex tones is widely believed to rely on temporal fine structure information conveyed by the precise phase-locked responses of auditory-nerve fibers. However, accurate pitch perception remains possible even when spectrally resolved harmonics are presented at frequencies beyond the putative limits of neural phase locking, and it is unclear whether residual temporal information, or a coarser rate-place code, underlies this ability. We addressed this question by measuring human pitch discrimination at low and high frequencies for harmonic complex tones, presented either in isolation or in the presence of concurrent complex-tone maskers. We found that concurrent complex-tone maskers impaired performance at both low and high frequencies, although the impairment introduced by adding maskers at high frequencies relative to low frequencies differed between the tested masker types. We then combined simulated auditory-nerve responses to our stimuli with ideal-observer analysis to quantify the extent to which performance was limited by peripheral factors. We found that the worsening of both frequency discrimination and F0 discrimination at high frequencies could be well accounted for (in relative terms) by optimal decoding of all available information at the level of the auditory nerve. A Python package is provided to reproduce these results, and to simulate responses to acoustic stimuli from the three previously published models of the human auditory nerve used in our analyses.

Neural-scaled entropy predicts the effects of nonlinear frequency compression on speech perception

The Neural-Scaled Entropy (NSE) model quantifies information in the speech signal that has been altered beyond simple gain adjustments by sensorineural hearing loss (SNHL) and various signal processing. An extension of Cochlear-Scaled Entropy (CSE) [Stilp, Kiefte, Alexander, and Kluender (2010). J. Acoust. Soc. Am. 128(4), 2112-2126], NSE quantifies information as the change in 1-ms neural firing patterns across frequency. To evaluate the model, data from a study that examined nonlinear frequency compression (NFC) in listeners with SNHL were used because NFC can recode the same input information in multiple ways in the output, resulting in different outcomes for different speech classes. Overall, predictions were more accurate for NSE than CSE. The NSE model accurately described the observed degradation in recognition, and lack thereof, for consonants in a vowel-consonant-vowel context that had been processed in different ways by NFC. While NSE accurately predicted recognition of vowel stimuli processed with NFC, it underestimated them relative to a low-pass control condition without NFC. In addition, without modifications, it could not predict the observed improvement in recognition for word final /s/ and /z/. Findings suggest that model modifications that include information from slower modulations might improve predictions across a wider variety of conditions.