Variations in the Slope of the Psychometric Functions for Speech Intelligibility: A Systematic Survey

MacPherson, Alexandra; Akeroyd, Michael A.

doi:10.1177/2331216514537722

Cited by 65 publications

(55 citation statements)

References 168 publications

(97 reference statements)

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“…7 shows the effect of varying b True over the range 0.01-0:5 dB À1 ; this encompasses the range of slopes found by MacPherson and Akeroyd (2014). The upper plot confirms the earlier finding for setup D that all the algorithms converge to the correct SRT much more slowly when b True is low.…”

Section: B Parameter Sensitivitysupporting

confidence: 74%

“…For each setup, A-F, Table I lists the model parameters of the true PF (subscripted "True") and the model parameters assumed by the algorithms (subscripted "Alg"). Setup A represents typical parameter values for the PF: at / ¼ 0:5, the SRT is a True ¼ À5 dB and the slope is b True ¼ 0:075 dB À1 , the mean slope observed in MacPherson and Akeroyd (2014). The guess rate, c True ¼ 1%, and lapse rate, k True ¼ 2%, correspond to low token predictability and an attentive listener.…”

Section: A Convergence Evaluationsmentioning

confidence: 99%

“…The primary goal of a speech intelligibility test is generally to measure the speech-reception threshold (SRT / ), i.e., the SNR at which a particular fraction, /, of words are correctly identified (Plomp and Mimpen, 1979;Wilson et al, 2007). It is also, however, often important to measure the slope of the PF at the SRT (MacPherson and Akeroyd, 2014;Neuman et al, 2010) and it is this that determines the perceptual benefit that can be obtained by improving the SNR. This paper presents an adaptive procedure for measuring both the SRT and the slope of the PF.…”

Section: Introductionmentioning

confidence: 99%

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Robust and efficient Bayesian adaptive psychometric function estimation

Doire

Brookes

Naylor

2017

The Journal of the Acoustical Society of America

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The efficient measurement of the threshold and slope of the psychometric function (PF) is an important objective in psychoacoustics. This paper proposes a procedure that combines a Bayesian estimate of the PF with either a look one-ahead or a look two-ahead method of selecting the next stimulus presentation. The procedure differs from previously proposed algorithms in two respects: (i) it does not require the range of possible PF parameters to be specified in advance and (ii) the sequence of probe signal-to-noise ratios optimizes the threshold and slope estimates at a performance level, ϕ, that can be chosen by the experimenter. Simulation results show that the proposed procedure is robust and that the estimates of both threshold and slope have a consistently low bias. Over a wide range of listener PF parameters, the root-mean-square errors after 50 trials were ∼1.2 dB in threshold and 0.14 in log-slope. It was found that the performance differences between the look one-ahead and look two-ahead methods were negligible and that an entropy-based criterion for selecting the next stimulus was preferred to a variance-based criterion

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Section: B Parameter Sensitivitysupporting

confidence: 74%

Section: A Convergence Evaluationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust and efficient Bayesian adaptive psychometric function estimation

Doire

Brookes

Naylor

2017

The Journal of the Acoustical Society of America

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“…The reference SRT of a speech test in noise is a test-specific parameter that depends on multiple test-specific variables such as speech material, scoring method, speaker, and speech rate [4,19]. The comparison between the Finnish tests is interesting because, unlike with many other DTTs and matrix tests, the speech material for both tests was recorded during the same recording session and with the same trained speaker using a constant speech rate.…”

Section: Discussionmentioning

confidence: 99%

The development and evaluation of the Finnish digit triplet test

Willberg

Buschermöhle

Sivonen

et al. 2016

Acta Oto-Laryngologica

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Objectives: The aim of the study was to develop a reliable and easily accessible screening test for primary detection of hearing impairment. Methods: Digits 0-9 were used to form quasirandom digit triplets. First, digit specific intelligibility functions and speech recognition thresholds (SRTs) were determined. To homogenize the test material digits with steep intelligibility function slopes were chosen and level correction up to ±2 dB were applied to the digits as needed. Evaluation measurements were performed to check for systematic differences in intelligibility between the test lists and to obtain normative reference function for normal-hearing listeners.Results: The mean SRT and the final slope of the test lists were À10.8 ± 0.1 dB signal-to-noise ratio (SNR) and 21.7 ± 1.8%/dB, respectively (measurements at constant level; inter-list variability). The mean SRT and slope of the test subjects were À10.8 ± 0.5 dB SNR and 23.4 ± 5.2%/dB (measurements at constant level; inter-subject variability). The mean SRT for normal-hearing young adults for a single adaptive measurement is À9.8 ± 0.9 dB SNR. Conclusion: The Finnish digit triplet test is the first self-screening hearing test in the Finnish language. It was developed according to current standards, and it provides reliable and internationally comparable speech intelligibility measurements. ARTICLE HISTORY

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“…The positive correlation between speech perception and long-term SNR (i.e., levels averaged over 30 seconds) has been well documented when the SNR is modified at the input (e.g., Cox et al, 1987, 1988; for a review, see MacPherson and Akeroyd, 2014). However, measuring the SNR at the output of a HA is difficult to achieve due to mixing of the speech and noise signals.…”

Section: Introductionmentioning

confidence: 99%

Output signal-to-noise ratio and speech perception in noise: effects of algorithm

Miller

Bentler

et al. 2017

International Journal of Audiology

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Objective The aim of this study was to: 1) quantify the amount of change in signal-to-noise ratio (SNR) as a result of compression and noise reduction processing in devices from three hearing aid (HA) manufacturers, and 2) use the SNR changes to predict changes in speech perception. We hypothesized that the SNR change would differ across processing type and manufacturer, and that improvements in SNR would relate to improvements in performance. Design SNR at the output of the HAs was quantified using a phase-inversion technique. A linear mixed model was used to determine whether changes in SNR across HA conditions were predictive of changes in aided speech perception in noise. Study Sample Two groups participated: 25 participants had normal hearing and 25 participants had mild to moderately-severe sensorineural hearing loss. Results The HAs programmed for both groups changed the SNR by a small, but statistically significant amount. Significant interactions in SNR changes were observed between HA devices and processing types. However, the change in SNR was not predictive of changes in speech perception. Conclusion Although observed significant changes in SNR resulting from compression and noise reduction did not convert to changes in speech perception, these algorithms may serve other purposes.

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Variations in the Slope of the Psychometric Functions for Speech Intelligibility: A Systematic Survey

Cited by 65 publications

References 168 publications

Robust and efficient Bayesian adaptive psychometric function estimation

Robust and efficient Bayesian adaptive psychometric function estimation

The development and evaluation of the Finnish digit triplet test

Output signal-to-noise ratio and speech perception in noise: effects of algorithm

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