Suprathreshold Differences in Competing Speech Perception in Older Listeners With Normal and Impaired Hearing

Abstract

Purpose Age-related declines in auditory temporal processing and cognition make older listeners vulnerable to interference from competing speech. This vulnerability may be increased in older listeners with sensorineural hearing loss due to additional effects of spectral distortion and accelerated cognitive decline. The goal of this study was to uncover differences between older hearing-impaired (OHI) listeners and older normal-hearing (ONH) listeners in the perceptual encoding of competing speech signals. Method Age-matched groups of 10 OHI and 10 ONH listeners performed the coordinate response measure task with a synthetic female target talker and a male competing talker at a target-to-masker ratio of +3 dB. Individualized gain was provided to OHI listeners. Each listener completed 50 baseline and 800 “bubbles” trials in which randomly selected segments of the speech modulation power spectrum (MPS) were retained on each trial while the remainder was filtered out. Average performance was fixed at 50% correct by adapting the number of segments retained. Multinomial regression was used to estimate weights showing the regions of the MPS associated with performance (a “classification image” or CImg). Results The CImg weights were significantly different between the groups in two MPS regions: a region encoding the shared phonetic content of the two talkers and a region encoding the competing (male) talker's voice. The OHI listeners demonstrated poorer encoding of the phonetic content and increased vulnerability to interference from the competing talker. Individual differences in CImg weights explained over 75% of the variance in baseline performance in the OHI listeners, whereas differences in high-frequency pure-tone thresholds explained only 10%. Conclusion Suprathreshold deficits in the encoding of low- to mid-frequency (~5–10 Hz) temporal modulations—which may reflect poorer “dip listening”—and auditory grouping at a perceptual and/or cognitive level are responsible for the relatively poor performance of OHI versus ONH listeners on a different-gender competing speech task. Supplemental Material https://doi.org/10.23641/asha.12568472

Speech-Driven Spectrotemporal Receptive Fields Beyond the Auditory Cortex

“…After scanning, the filters themselveseach summarizing the spectrotemporal patterns retained in each sentenceare weighted by the response elicited by the corresponding sentence and summed to produce a STRF. Essentially, the weighted sum is a linear model (cf., Venezia, Hickok, & Richards, 2016;Venezia, Leek, & Lindeman, 2020;Venezia, Martin, Hickok, & Richards, 2019a) whose predictors are the binary filters and whose criterion is the sentence-by-sentence fMRI response magnitude (i.e., event-related beta time series). A schematic of this process is shown in Figure 1C.…”

Reward Enhances Online Participants’ Engagement With a Demanding Auditory Task

“…These paradigms have been shown to be powerful tests of listening in complex environments because of their sensitivity to small intelligibility changes in highly noisy backgrounds, their applicability to testing with different maskers, and their relative independence from semantic/syntactic cues ( Brungart, 2001 ; De Sousa et al., 2020 ; Eddins & Liu, 2012 ; Humes et al., 2017 ). Accumulating work demonstrates that speech reception thresholds (SRTs) estimated with an adaptive CRM task correlate with audiometric thresholds and with age ( de Kerangal et al., 2020 ; Schoof & Rosen, 2014 ; Venezia et al., 2020 ), rendering it a potentially efficient proxy of hearing ability ( Semeraro et al., 2017 ). An additional advantage is that the task relies on manipulating the relative intensity of the target and the masker, and performance is largely independent of overall level over a reasonable range.…”

Mechanisms of Spectrotemporal Modulation Detection for Normal- and Hearing-Impaired Listeners

“…There is now converging evidence that the human auditory system relies on MPS representations to analyze complex suprathreshold signals such as speech. Physiological studies have shown that the central auditory system exhibits specialized tuning to STMs ( Hullett et al., 2016 ; Santoro et al., 2017 ), and behavioral studies have demonstrated that speech intelligibility is conveyed by STMs within specific ranges of temporal (1–10 Hz) and spectral (1–2 cycl/oct) modulations ( Elliott & Theunissen, 2009 ; Venezia et al., 2016 , 2020 ). Furthermore, results from modeling studies have shown that cortical auditory models or metrics such as the STM index, all based on decomposition of auditory signals through an STM filter bank, provide accurate accounts of SIN intelligibility scores ( Bernstein et al., 2013b ; Chi et al., 1999 ; Elhilali et al., 2003 ).…”