A binaural model predicting the effect of hearing impairment and noise levelon speech intelligibility∗Thibault VICENTE(1,2) , Mathieu LAVANDIER(1) , Jörg M. BUCHHOLZ(2)(1) Univ Lyon, ENTPE, Laboratoire Génie Civil et Bâtiment, Rue Maurice Audin, 69518 Vaulx-en-Velin Cedex, France(2) Department of Linguistics - Audiology, Australian Hearing Hub, Macquarie University, 2109 NSW, Australia

Abstract
A model will be presented that allows predicting binaural speech intelligibility in noise for normal-hearing (NH)and hearing-impaired (HI) listeners. The model inputs are the masker and target signals at the listener’s ears, aswell as their audiogram that is used to implement an internal noise (IN). The model computes per time frame andfrequency band a binaural masking level difference (BMLD) when all signal levels are higher than the IN level,
and the signal-to-noise ratio at the better ear (BE-SNR) using the maximum between the masker and IN levels.
The BMLD and BE-SNR are integrated across time and frequency and summed to obtain a binaural ratio.
The relative differences of the binaural ratios are compared to speech reception threshold (SRT) differences
measured in listening tests. The model was validated on one experiment involving NH and HI listeners. Stimuli
were anechoic and played using headphones. The target, in front of the listener, was presented simultaneouslywith two noise-vocoded speech maskers either collocated with the target or separated at +/- 90◦ . The noise waspresented at different sensation levels and the relative target level was adapted to derive SRTs. The experimental
data were accurately predicted by the model.
Keywords: Model, Speech Intelligibility, Binaural, Hearing Impairment

1INTRODUCTION

Having two ears is helpful to improve target intelligibility in noisy environment. A perceptual mechanism
well-know as "Spatial Release from Masking" (SRM) quantifies our ability to benefit from binaural cues, theinteraural level differences (ILD) and interaural time differences (ITD) (e.g. [7]), to improve target intelligibility
when it is spatially separated from the masker. However, SRM is reduced for HI listeners [8].
In order to predict the influence of the noise on speech intelligibility, several models have been developed for
NH listeners [16, 5] and for HI listeners [4, 10]. The study will focus on the model developed by Lavandier etal. [10], which allowed to predict binaural speech intelligibility in the presence of envelope modulated noises
for NH and HI listeners. However, they had to use two different model parameter values for each of the twolistener groups to obtain good predictions, effectively resulting actually in two different models. The current
study proposes a modification to the concept of the IN (i.e, the model component that describes the listener’shearing ability) allowing to predict data involving NH and HI listeners [14] with a single model.

2MODEL DESCRIPTION

An updated version of the model developed by Lavandier et al. [10] is presented in this study which allows
predicting the effect of hearing impairment on binaural speech intelligibility for NH and HI listeners. In brief,masker and target signals at the listener’s ears are taken as inputs for the model as well as the listener’s
audiogram in order to build an IN which is considered as another masker. Based on the incoming signals andthe IN, the binaural unmasking advantage and the BE-SNR are computed per time frame and frequency band,∗ Corresponding address: thibault.vicente@entpe.fr2197

averaged across time and frequency, and added to obtain a binaural ratio. The differences between binaural
ratios are then compared to the differences between the SRTs measured in the listening test.
The target energy is averaged across time to avoid that a lack of energy leads to a low signal-to-noise ratio(SNR) and thus, a poor intelligibility, even though it could correspond to a pause between two words, which
provides relevant information for the listener. The masker signal energy is taken into account as a function oftime to allow the model to predict the ability of a listener to understand speech in the dips of the masker’senvelope. The long-term characteristics (i.e., spectra at each ear and interaural phase differences) of the target
are computed once, and then combined with those of the masker resulting from the time-frame analysis. Before
all signals are passed through a gammatone filterbank with two filters per equivalent rectangular bandwidth, themasker signals are segmented using a 24-ms and also a 300-ms half-overlapping Hann window [15].
The IN is implemented based on the listener’s audiogram. The binaural unmasking advantages are computed byapplying the BMLD formula from [6] on the signals from the longer time window only if the masker and target
levels are above the IN level. On the other hand, the BE-SNR is computed choosing the higher SNR between
the left and right ear (with a ceiling SNR value of 20 dB to avoid that the SNR goes to infinity in the masker’sdips) while considering the higher level between the IN and the masker signal using the shorter window. Then,the model averages across time and frequency (using a SII weighting, [1]) the binaural unmasking advantages
and BE-SNRs and adds these values to obtain the binaural ratio.
In order to compare the target and masker signals and the listener’s IN, the signals are set at the actual levels
played during the experiment, in dB SPL. As for the design of the IN spectrum at each ear, three assumptions
are made, (1) the overall level of the external stimuli is approximated by the masker level, in other words it
is assumed that the broadband SNR is below 0 dB, (2) the hearing loss (HL) is split into X % of outer haircell loss (HLOHC ) and 1-X % of inner hair cell loss (HLIHC ) at each frequency and (3) the maximum allowedfor HLOHC is 57.6 dB [11], the loss above this value contributes to the HLIHC . The HLOHC and HLIHC areinterpolated at the model’s center frequencies between the lower and upper center frequency of the audiogramand extrapolated otherwise, using a logarithmic frequency scale. The IN is then calculated using the following
formula:N−Nlim+(1−X)HL(n)B10)(1)
InternalNoise(n, N) = audX (n) + 10 ∗ log10(10 10 + 10where, n is the center frequency of the nth frequency band and N is the overall (external) masker level (averaged across listerner’s ears). The term audX is defined by audX (n) = HL2SPL(n) + X ∗ HL(n) = HL2SPL(n) +HLOHC (n) in dB SPL, with HL2SPL the transformation to convert hearing loss from dB HL into dB SPL. Thistransformation results from the sum of the reference equivalent sound pressure levels for the THD 39 headphones applied in measuring the subject’s audiograms [9] and nominal values for the transformation from 6 cc
coupler to ear drum levels [3]. The transformation is interpolated between 200 Hz and 6 300 Hz (the range
where values are available) and extrapolated otherwise to get the transformation values at the center frequenciesof the model. The term (1 − X)HL(n) represents the HLIHC , in dB HL. The free parameters B, Nlim, X weretested before being set at -10 dB, 83 dB and 70%, respectively (see section 4).
For the predictions of the data presented below, the target signal was created by averaging the waveforms of 128
sentences used during the listening test. All sentences were thereby truncated to the shortest sentence duration.
The duration of each masker signal was 2 minutes. All signals were convolved with the impulse response ofthe (equalized) headphones used for data collection and measured on a 4128C Bruel&Kjaer head and torso
simulator. Then all signals were calibrated to the sound levels used in the experiment.

3DATA

To validate the model, an experiment was used that specially investigated the effect of audibility on SRT [14].
Ten NH listeners (mean age of 23.2 +/- 3.2 years, hearing loss <15 dB HL) participated in the experiment
as well as ten sensorineural HI listeners (mean age of 70.3 +/- 7.8 years). They had a symmetric (thresholddifference between ears <10 dB) mild to moderate, sloping sensorineural hearing loss. Their four-frequency

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5SRT (dB)

0-5HI listeners

NH listeners

colloc (data)
separ (data)
colloc (model)
separ (model)

colloc (data)
separ (data)
colloc (model)
separ (model)-10-15-20-25r = 0.98
MeanErr = 1 dB
MaxErr = 3.1 dB

010

2030Masker Level (dB SL)

Figure 1. Mean SRTs with standard errors across the NH listeners (in grey symbols) and HI listeners (in blacksymbols) measured and predicted for four overall masker sensation levels. The two maskers were two noisevocoded speech either placed at the same position as the target in front of the listener (colloc) or on each sideof the listener at +/- 90◦ (separ).
(0.5, 1, 2, 4 kHz) average hearing loss (4FAHL) was 29.1 +/- 8.0 dBHL. All participants were English nativespeakers.
The SRTs were measured using BKB-like sentences [2] and an adaptative procedure. The target was simulated
in front of the listener. Two noise-vocoded speech maskers were simultaneously presented with the target either
collocated with it or symmetrically placed on each side of the listener at +/- 90◦ . The frontal position wassimulated by convolving the anechoic stimuli with an head-related transfer function for frontal incidence averaged across ears, resulting in diotic listening. The spatially separated configuration was simulated by presenting
the left masker only through the left headphone and the right masker through the right headphone. Target
and maskers signals were filtered, considering nine frequency regions, in order to equalize individual audibility
across listeners and they were played at four different sensation levels (0, 10, 20, 30 dB SL, reference: individual SRT in quiet). However, some HI listeners could not be tested for some of the higher sensation levels
due to loudness discomfort. All stimuli were presented through the Sennheiser HD215 circumaural headphones
and were filtered in order to have the same long-term spectrum as the averaged spectrum of the 1280 sentencesfrom the corpus after applying the above diotic spatialization process.
Figure 1 shows measured SRTs plotted as a function of the masker level in dB SL. The grey and black circles
represent the measured SRTs for the NH and HI listeners, respectively. The collocated configuration (colloc)
is displayed as filled circles and as open circles for the separated configuration (separ). Increasing the overall
sensation level always led to lower SRTs (i.e., better intelligibility) as well as an increase in SRM as measured
by the difference in SRTs between the collocated and spatially separated conditions. No significant SRT differences between the two groups of listeners were found, suggesting that performance was similar across groups
when audibility was controlled carefully.

4MODEL PREDICTIONS

The model predictions are plotted in figure 1 using solid and dashed lines corresponding to the spatially collocated and separated conditions, respectively, either in grey for the NH listeners or in black for the HI listeners.

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First, in order to find the best combination of parameters in formula 1 (i.e. B, Nlim and X), three indices ofmodel performance were used: the Pearson’s correlation coefficient r between data and prediction, the mean
absolute error (MeanErr, computed as the average across conditions of the absolute difference between dataand prediction) and the maximum absolute error (MaxErr, computed as the maximum of the absolute difference
between data and predictions across all conditions). The free parameters B, Nlim and X were varied within
ranges [-16;-8] dB, [65;85] dB and [60;90]% [12], respectively in order to simultaneously minimize the twotypes of error and maximize the correlation. The best predictions were obtained with the values of B, Nlim andX equal to -10 dB, 83 dB and 70 %, respectively. Using these values, the model predicts well the data with acorrelation, mean absolute error and maximum absolute error equal to 0.98, 1 dB and 3.1 dB, respectively.

5DISCUSSION

The revised model allowed to accurately predict the effect of audibility on speech intelligibility for NH and HI
listeners, using a single model. This is in contrast to the previous version of the model, developed by Lavandier
et al. [10], which required a different parameter value for the IN to predict the effect of audibility on speech
intelligibility for NH listeners as well as for HI listeners, which effectively resulted in two different models. The
new implementation of the IN depends on the external noise level and takes into account the different effects
of inner and outer hair cell loss on speech intelligibility. The resulting model provided intelligibility predictions
that were as accurate as the ones provided by the two previous models [10].
Considering these encouraging results, the revised model needs to be further validated using additional intelligibility data. For instance, the data presented by [13] could be used to evaluate the performance of the modelto predict the difference in both SRT and SRM between stationary speech-shaped noise and (modulated) noisevocoded speech maskers for NH as well as HI listeners. Given that the stimuli that were used so far forevaluating the proposed model did not involve realistic ITDs, future research will need to specifically evaluate the model-internal processes that are related to ITDs. In particular, this should include an analysis of thepotential effect of reduced ITD sensitivity in HI listeners on binaural unmasking.

ACKNOWLEDGEMENTS
The PhD of Thibault Vicente is funded by la Fondation pour l’Audition and Macquarie University (throughan international research excellence scholarship, iMQRES). The participation of Thibault Vicente at ICA 2019was funded by la Fondation pour l’Audition, la Société Française d’Acoustique and by the ICA-ASA-DEGA
Young Scientist Conference Attendance Grants (YS Grant). This work was performed within the Labex CeLyA(ANR-10-LABX-0060/ANR-16-IDEX-0005).

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