The Effect of Background Noise on Intelligibility of Dysphonic Speech

Ishikawa, Keiko; Boyce, Suzanne; Kelchner, Lisa; Powell, Maria Golla; Schieve, Heidi; Alarcón, Alessandro de; Khosla, Sid

doi:10.1044/2017_jslhr-s-16-0012

Cited by 45 publications

(47 citation statements)

References 36 publications

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“…Despite this, a similar pattern of decrease was observed for both control and dysarthric speech, which is in accord with the findings of the current study. Alternatively, the pattern of results for the effect of noise on dysphonic speech appears to display a more multiplicative effect than that of noise on control speech (Ishikawa et al, 2017). However, the absence of statistical analysis of this interaction renders it difficult to conclude this definitively.…”

Section: Discussionmentioning

confidence: 97%

“…Despite a somewhat implicit assumption of a multiplicative effect of noise in listener perception of degraded speech, the results of the current study do not stand in obvious contrast to the results of other, related studies on the matter. However, it is difficult to conclude this entirely, as there have been several factors precluding a thorough and complete analysis of the effect of noise on degraded speech in previous findings, such as ceiling and/or floor effects (Munro, 1998;Rogers et al, 2004;Dykstra et al, 2012), no statistical analysis of the interaction effect (Ishikawa et al, 2017), not controlling for SNR (Adams et al, 2008), or simply that the study did not measure intelligibility directly (McColl et al, 1998). The initial data available on the effect of noise on dysarthric speech specifically appears to be in agreement with the current findings.…”

Section: Discussionmentioning

confidence: 97%

“…The effect of noise on the intelligibility of dysphonic speech-a speech signal characterized by auditory perceptual features of disordered voicing including roughness, breathiness, and strainhas also been examined. Ishikawa et al (2017) presented listeners with speech samples from speakers with typical speech and speakers with dysphonia, in quiet conditions and at two SNRs (þ5 and 0 dB). As expected, the dysphonic speech was significantly less intelligible than the typical speech, and there was a significant effect of SNR.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of studies in this area appear to simply show a shift in intelligibility for source-degraded speech in noise which parallels the shift in intelligibility for the sourcedegraded speech in quiet conditions. Unfortunately, these results are often obscured by the presence of ceiling or floor effects in the data (e.g., Munro, 1998;Ishikawa et al, 2017;Dykstra et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Combining degradations: The effect of background noise on intelligibility of disordered speech

Yoho

Borrie

2018

The Journal of the Acoustical Society of America

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The effect of background noise on intelligibility of disordered speech was assessed. Speech-shaped noise was mixed with neurologically healthy (control) and disordered (dysarthric) speech at a series of signal-to-noise ratios. In addition, bandpass filtered control and dysarthric speech conditions were assessed to determine the effect of noise on both naturally and artificially degraded speech. While significant effects of both the amount of noise and the type of speech were revealed, no interaction between the two factors was observed, in either the broadband or filtered testing conditions. Thus, it appears that there is no multiplicative effect of the presence of background noise on intelligibility of disordered speech relative to control speech. That is, the decrease in intelligibility due to increasing levels of noise is similar for both types of speech, and both types of testing conditions, and the function for dysarthric speech is simply shifted downward due to the inherent source degradations of the speech itself. Last, large-scale online crowdsourcing via Amazon Mechanical Turk was utilized to collect data for the current study. Findings and implications for this data and data collection approach are discussed.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Combining degradations: The effect of background noise on intelligibility of disordered speech

Yoho

Borrie

2018

The Journal of the Acoustical Society of America

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“…Children are at risk of underachievement when trying to understand a dysphonic voice, especially when the task itself is less challenging or the child’s cognitive capacity is stretched ( Lyberg-Åhlander et al, 2015 ). Moreover, a talker with a dysphonic voice may be even harder to understand than a normal voice when there is background noise ( Ishikawa et al, 2017 ) or the talker may be perceived to be less intelligent and less socially attractive ( Eadie et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Listening Comprehension and Listening Effort in the Primary School Classroom

et al. 2018

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In the primary school classroom, children are exposed to multiple factors that combine to create adverse conditions for listening to and understanding what the teacher is saying. Despite the ubiquity of these conditions, there is little knowledge concerning the way in which various factors combine to influence listening comprehension and the effortfulness of listening. The aim of the present study was to investigate the combined effects of background noise, voice quality, and visual cues on children’s listening comprehension and effort. To achieve this aim, we performed a set of four well-controlled, yet ecologically valid, experiments with 245 eight-year-old participants. Classroom listening conditions were simulated using a digitally animated talker with a dysphonic (hoarse) voice and background babble noise composed of several children talking. Results show that even low levels of babble noise interfere with listening comprehension, and there was some evidence that this effect was reduced by seeing the talker’s face. Dysphonia did not significantly reduce listening comprehension scores, but it was considered unpleasant and made listening seem difficult, probably by reducing motivation to listen. We found some evidence that listening comprehension performance under adverse conditions is positively associated with individual differences in executive function. Overall, these results suggest that multiple factors combine to influence listening comprehension and effort for child listeners in the primary school classroom. The constellation of these room, talker, modality, and listener factors should be taken into account in the planning and design of educational and learning activities.

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Hey Siri: How Effective are Common Voice Recognition Systems at Recognizing Dysphonic Voices?

et al. 2020

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Objectives/Hypothesis: Interaction with voice recognition systems, such as Siri™ and Alexa™, is an increasingly important part of everyday life. Patients with voice disorders may have difficulty with this technology, leading to frustration and reduction in quality of life. This study evaluates the ability of common voice recognition systems to transcribe dysphonic voices.Study Design: Retrospective evaluation of "Rainbow Passage" voice samples from patients with and without voice disorders.Methods: Participants with (n = 30) and without (n = 23) voice disorders were recorded reading the "Rainbow Passage". Recordings were played at standardized intensity and distance-to-dictation programs on Apple iPhone 6S™, Apple iPhone 11 Pro™, and Google Voice™. Word recognition scores were calculated as the proportion of correctly transcribed words. Word recognition scores were compared to auditory-perceptual and acoustic measures.Results: Mean word recognition scores for participants with and without voice disorders were, respectively, 68.6% and 91.9% for Apple iPhone 6S™ (P < .001), 71.2% and 93.7% for Apple iPhone 11 Pro™ (P < .001), and 68.7% and 93.8% for Google Voice™ (P < .001). There were strong, approximately linear associations between CAPE-V ratings of overall severity of dysphonia and word recognition score, with correlation coefficients (R 2 ) of 0.609 (iPhone 6S™), 0.670 (iPhone 11 Pro™), and 0.619 (Google Voice™). These relationships persisted when controlling for diagnosis, age, gender, fundamental frequency, and speech rate (P < .001 for all systems).Conclusion: Common voice recognition systems function well with nondysphonic voices but are poor at accurately transcribing dysphonic voices. There was a strong negative correlation with word recognition scores and perceptual voice evaluation. As our society increasingly interfaces with automated voice recognition technology, the needs of patients with voice disorders should be considered.

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The Effect of Background Noise on Intelligibility of Dysphonic Speech

Cited by 45 publications

References 36 publications

Combining degradations: The effect of background noise on intelligibility of disordered speech

Combining degradations: The effect of background noise on intelligibility of disordered speech

Listening Comprehension and Listening Effort in the Primary School Classroom

Hey Siri: How Effective are Common Voice Recognition Systems at Recognizing Dysphonic Voices?

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