Abstract
Previous work by McAuley et al. Attention, Perception, & Psychophysics, 82, 3222–3233, (2020), Attention, Perception & Psychophysics, 83, 2229–2240, (2021) showed that disruption of the natural rhythm of target (attended) speech worsens speech recognition in the presence of competing background speech or noise (a target-rhythm effect), while disruption of background speech rhythm improves target recognition (a background-rhythm effect). While these results were interpreted as support for the role of rhythmic regularities in facilitating target-speech recognition amidst competing backgrounds (in line with a selective entrainment hypothesis), questions remain about the factors that contribute to the target-rhythm effect. Experiment 1 ruled out the possibility that the target-rhythm effect relies on a decrease in intelligibility of the rhythm-altered keywords. Sentences from the Coordinate Response Measure (CRM) paradigm were presented with a background of speech-shaped noise, and the rhythm of the initial portion of these target sentences (the target rhythmic context) was altered while critically leaving the target Color and Number keywords intact. Results showed a target-rhythm effect, evidenced by poorer keyword recognition when the target rhythmic context was altered, despite the absence of rhythmic manipulation of the keywords. Experiment 2 examined the influence of the relative onset asynchrony between target and background keywords. Results showed a significant target-rhythm effect that was independent of the effect of target-background keyword onset asynchrony. Experiment 3 provided additional support for the selective entrainment hypothesis by replicating the target-rhythm effect with a set of speech materials that were less rhythmically constrained than the CRM sentences.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgments
The authors thank Frank Dolecki and Kyle Oliver for their assistance with data collection and helpful insights, Dylan V. Pearson at Indiana University for assistance with stimulus generation, and members of the Timing, Attention and Perception Lab at Michigan State University for their helpful suggestions and comments at various stages of this project. NIH Grant R01DC013538 (PIs: Gary R. Kidd and J. Devin McAuley), NIH Grant R01017988 (PI: Yi Shen), and the University of Washington Mary Gates Undergraduate Research Scholarship (Awardee: Christina N. Williams; Mentor: Yi Shen) supported this research.
Funding
This work was supported by NIH Grant R01DC013538 (PIs: Gary R. Kidd and J. Devin McAuley), NIH Grant R01017988 (PI: Yi Shen), and the University of Washington Mary Gates Undergraduate Research Scholarship (Awardee: Christina N. Williams; Mentor: Yi Shen)
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Appendices
Appendix 1
Experiment 1: Supplementary results
Learning analysis
At the suggestion of a reviewer, we investigated whether there were any learning effects on performance. Since intact and rhythm-altered conditions were alternated from one block to the next and there was no interaction between rhythm condition and block, F(7, 98) = 1.88, p = .082, η2 = 0.118, we averaged proportion correct data over pairs of blocks. There was a small but significant learning effect, F(7, 98) = 2.86, p = .009, η2 = 0.170. Follow-up Bonferroni-corrected paired t tests showed that the only comparison that approached significance was between the first (M = 0.39, SD = 0.09) and last (M = 0.49, SD = 0.12) blocks, t(14) = −3.62, p = .077, 95% CI [−0.22, 0.01]. No other differences approached significance (all p > .10).
NEQ and SSQ
Supplementary analyses considered the possible relation between individual scores on the NEQ and SSQ and overall speech-in-noise performance (as well as any relation to the magnitude of the target-rhythm effect) in Experiment 1. Estimates of annual noise exposure (ANE) were calculated for each participant from their NEQ responses, based on the procedure outlined in Johnson et al. (2017). The overall SSQ score was calculated by averaging responses across all items. Separate scores were also calculated for the speech, spatial, and qualities subscales of the SSQ by averaging individual responses across only items within one of the given subscales. The correlation between ANE and SSQ was significantly positive, r(13) = 0.56, p = .029. We found no relationship between estimates of ANE and overall speech-in-noise performance (averaged across target rhythm intact and target rhythm altered conditions), r(13) = −0.22, p = 0.44, nor between overall speech-in-noise performance and overall SSQ scores, r(13) = 0.33, p = 0.24. There also was no reliable correlation between overall speech-in-noise performance and any of the SSQ subscales—speech: r(13) = 0.33, p = .24; spatial: r(13) = 0.16, p = .58; qualities: r(13) = 0.30, p = .28.
In terms of the magnitude of the target-rhythm effect (the performance in the rhythm intact condition minus performance in the rhythm altered condition), there was similarly no relationship between ANE and the magnitude of the target-rhythm effect, r(13) = −0.08, p = .77, nor between SSQ scores and the magnitude of the target-rhythm effect, r(13) = 0.28, p = .31. There was also no correlation between the magnitude of the rhythm effect and any of the three SSQ subscales—speech: r(13) = 0.26, p = .35; spatial: r(13) = 0.27, p = .33; qualities: r(13) = 0.18, p = .53.
Finally, there was no evidence of a relationship between formal music training and speech-in-noise performance when overall performance was compared between participants who did not report having any formal music training (M = 0.45, SD = 0.08) and those who did (M = 0.46, SD = 0.08), t(13) = −0.42, p = .68, Cohen’s d = −0.22, 95% CI [−0.11, 0.07]. Similarly, the magnitude of the target-rhythm effect was not significantly different for participants with formal music training (M = 0.11, SD = 0.06) and without formal music training (M = 0.06, SD = 0.04), t(13) = −1.86, p = .09, Cohen’s d = −0.96, 95% CI [−0.10, 0.01].
Experiment 2: Supplementary results
Supplementary analyses considered the potential relations between estimated ANE from the NEQ, SSQ scores, formal music training and overall speech-in-noise performance. To create a composite speech-in-noise score, proportion correct data was averaged for each participant across OA and rhythm intact/altered conditions. Since there were different SNR groups, we conducted partial correlations between ANE, overall SSQ score, years of formal music training, and speech-in-noise performance, controlling for SNR. Three participants were excluded from these analyses due to data quality issues with their survey responses (e.g., responding “10” to all SSQ items, or having multiple inconsistent responses on the NEQ that may have led to underestimates of ANE). There was no correlation between overall performance and ANE, r(33) = 0.244, p = .16, between overall performance and overall SSQ score, r(33) = -0.116, p = .51, or between overall performance and years of formal music training, r(33) = −0.04, p = .80. When SSQ responses were broken down into three subscales, there was still no correlation with overall proportion correct scores—speech: r(33) = 0.038, p = .83; spatial: r(33) = −0.08, p = .67; qualities: r(33) = −0.193, p = .27.
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Smith, T.M., Shen, Y., Williams, C.N. et al. Contribution of speech rhythm to understanding speech in noisy conditions: Further test of a selective entrainment hypothesis. Atten Percept Psychophys 86, 627–642 (2024). https://doi.org/10.3758/s13414-023-02815-0
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DOI: https://doi.org/10.3758/s13414-023-02815-0