1. Introduction
Understanding speech in noise is difficult. It is particularly difficult for individuals occupationally exposed to noise, e.g. motorcycle professional drivers. The difficulty arises because the background noise contains energy in the same frequency regions as the speech (energetic masking [
1]). It also arises because noise exposure is an environmental stressor, and prolonged or excessive noise exposure can alter the auditory periphery structures, and may ultimately lead to noise-induced hearing loss (LePrell & Clavier, 2017; Henderson et al., 2006). However, individuals chronically exposed to noise, even with normal or near-normal audiometric thresholds, can exhibit difficulties understanding speech in noise (e.g. Hope et al., 2013; Alvord, 1983).
Recently, several research groups have explored the hypothesis that noise exposure can induce a selective synaptic loss at the synapses between the inner hair cells (IHC) and the low spontaneous rate auditory nerve fibers in the cochlea, often occurring with otherwise normal or near-to-normal audiograms (see the seminal paper by Kujawa & Liberman, 2009; for a review, see Kujawa & Liberman, 2016; Plack et al., 2014). This synaptopathy has also been called hidden hearing loss (term coined by Schaette & McAlpine, 2011), because its effect is supposed to be unrevealed by conventional audiometric measures. It is now widely assumed that clinical measures more sensitive than the conventional audiogram are needed (LePrell Brungart 2016). However, a gold standard of these new tests and best practices is still to be defined (LePrell & Brungart, 2016; Plack et al., 2016), in order to detect early signs of hearing deficits, and to implement better prevention programs.
These new tests should be defined in relation to the difficulties of speech understanding in noise. The evaluation of speech in noise performance in humans varies along a large variety of factors (type of target speech, type of masker, type of response, signal-to-noise ratio, type of paradigms, to name a few), each providing different insights into an individual's ability to process speech in noisy environments (review in LePrell & Clavier, 2017). An interesting way to differentiate them in the context of this study is by their lexical complexity (phoneme or syllable, words, and sentences). It has indeed been shown that this is one of the key factors to understand the relative influence of the cognitive processes underlying and correlated to the speech in noise tasks (review by Dryden et al., 2017). Classically, tests with phonemes are less sensitive to cognitive factors than sentence recognition tests. To understand the apparent discrepancy in the literature regarding the existence of noise-induced cochlear synaptopathy in humans, DiNino et al. (2022) showed that the choice of the target speech and the speech in noise task impact greatly on whether a relationship between the speech in noise performance and the assumed physiological proxies of synaptopathy ( Electrocochleography/Auditory Brainstem Response Wave I, Middle Ear Muscle Reflex) is observed. For instance, the tests with a low lexical complexity and which maximize the importance of fine temporal details were more likely to be correlated with proxy measures of synaptopathy in humans.
The list of statistically significant predictors of speech in noise performance is vast, especially for individuals exposed to noise. A systematic overview being largely beyond the scope of this paper, we chose to focus here instead on measures that would, in fine, be easily implemented in a prevention program – in addition to the conventional pure tone audiogram. Behaviorally, decline in auditory temporal abilities (e.g. amplitude- and frequency-modulation thresholds) has been linked to a decline in speech in noise performance (Strelcyk & Dau, 2009; Hopkins & Moore, 2011; Ruggles et al., 2011; Bharadwaj et al., 2015; Fullgrabe et al., 2015). There is also now ample evidence for an association between extended high-frequency (EHF) audiometry, defined as frequencies above 8 kHz, and speech perception difficulties (review by Lough & Plack, 2022). Interestingly here, noise exposure has been identified as one of the causes of EHF hearing loss (LePrell et al., 2013; Prendergast et al., 2017). To complement behavioral audiometric measures, electrophysiological measures of the cochlear function can be performed in individuals with normal hearing thresholds, and compared with speech-in-noise performance (e.g. Grant et al., 2020; Bramhall, 2021; Parker, 2020). These tests include the measure of the cochlear amplification function via the measure of distortion product otoacoustic emission (DPOAE), or the synaptic activity between the IHC and the auditory nerve via the measure of electrocochleography (EcochG). As explained before for the speech-in-noise tasks, the different and sometimes opposite results in the literature regarding the existence and the measure of noise-induced cochlear synaptopathy can also be linked to the very heterogenous methods used (see the recent reviews by Bharadwaj et al., 2019; Bramhall et al., 2019; Le Prell, 2019). The discrepancy in the literature could also highlight the fact that a variability in what we call normal thresholds or near-normal thresholds can be responsible for some of the so-called synaptopathy effects. In some of the studies in which noise exposure seems responsible for functional speech in noise differences in the absence of hearing loss, there is, of course, the possibility that differences in thresholds within the normal range can contribute nonetheless to the differences observed in the speech in noise performance (LePrell, Clavier, 2017; Plack et al, 2014). In addition, cochlear synaptopathy is indeed very hard to study in humans, and is generally mixed with other outer hair cells (OHC) dysfunctions (Parker, 2020).
Finally, when studying noise exposed individuals, the definition itself and the measure of what is called “exposure” is crucial. As pointed out by Parker (2020), one of the differences potentially explaining the discrepancies between noise-induced cochlear synaptopathy studies lies in the way noise exposure is measured. When noise exposure measure is based on self-reports, no link is found between proxy of cochlear synaptopathy and speech-in-noise performance [
2,
3,
4]. When controlled and homogeneous groups of individuals exposed to noise are studied (young and professional musicians in Liberman et al., 2016; firearms users in Bramhall et al., 2017; train drivers, in Kumar et al., 2012), a correlation was found. Moreover, to investigate the effect of noise exposure, very often, groups of noise-exposed vs. control are compared. This could be in contradiction with the idea that the outcome of a noise exposure is possibly on a continuum from non-synaptopathy to synaptopathy with damage (LePrell and Brungart, 2016).
In the current study, we do not question the influence of different predictors on speech in noise performance, nor test the influence of one predictor on another (although it would be possible with this set of data). Instead, we investigate how to quantify and classify the various predictors of speech-in-noise performance in terms of importance. This approach has a direct clinical outcome as it allows us to establish which predictors are urgently needed for regular testing (LePrell & Clavier, 2017) in order to enhance existing hearing-loss prevention policies.
In fact, the choice of the statistical model and analysis are key to our study design as they influence the way we think about the design, as well as the conclusions we can draw from the data. To illustrate this point, Yeend et al (2017) recognized that one of the limitations of their study is the use of multiple comparisons, potentially resulting in falsely identified positive effects. More recently, Balan et al (2023) emphasized, with appropriate machine-learning techniques, the importance of EHF audiogram in predicting speech in noise performance.
In this paper, we use random forests - a machine-learning tool for classification and regression. The random forest tool is intuitive, and, more importantly, it has an inherent capacity of producing measures of “variable importance”. Kim et al. (2021) highlighted the usefulness of the random forest model, compared to other machine learning techniques, for predicting speech discrimination scores from pure tone audiometry thresholds.
In this study, we investigated the relative importance of several audiometric, auditory, physiological predictors on speech-in-noise performance. Speech-in-noise was assessed with 3 different speech audiometry in noise, with different degrees of lexical complexity (consonant identification; word in noise recognition; French sentence matrix test). Our listeners group consists of individuals exposed to occupational noise: professional motorcyclists. This allowed us to have a homogeneous subject group and an easy proxy measure of noise exposure (number of years of motorcycling). All participants had normal hearing thresholds (Pure Tone Average (PTA) : mean of thresholds at 500, 1000, 2000, and 4000 Hz, inferior to 20 dB HL according to the reference of the International Bureau for Audiophonologie [
5]. For all participants, we measured EHF audiometry at 12.5 kHz; DPOAE to evaluate OHC function; and EcochG to assess auditory nerve function. Temporal processing was assessed using amplitude- (AM) and frequency-modulation (FM) detection thresholds. Finally, we evaluated the subjective auditory consequences of each subject’s noise exposure via the speech in noise pragmatic scale of the Speech, Spatial and Qualities of Hearing Scale (SSQ) questionnaire [
6,
7].
2. Materials and Methods
2.1. Overview
The experiment was conducted over a week during a span of 3 half-days, each dedicated to specific sets of experimental sessions. Two half-days were composed of a speech audiometry in noise test and a behavioral test (for instance, a session of consonants identification followed by a session of AM detection). The third one was composed of a speech audiometry in noise test, the recordings of DPOAE and EcochG, and the questionnaires (demographic and SSQ speech in noise pragmatic scale). The order of all tests was randomized with at least one speech-in-noise test during each half-day.
2.2. Participants
Seventy-three participants (72 men; mean and standard deviation age: 38 ±7.6 years (
Figure 1) took part in the present study. All were professional bikers occupationally exposed to motorcycle noise (duration of exposure ranging between 1 year and 31 years, with a median duration of 8 years). Three participants were excluded because of their PTA above or equal to 20 dB HL. The 70 remaining participants had PTA considered normal in both ears according to Internation Bureau for Audiophonology calculation [
5]. Maximal age was limited to 55 years to reduce the risk of presbycusis. Informed consent was obtained from all participants involved in the study. This study was approved by Comité de protection des personnes sud-ouest et outre-mer II (IDRCB 2017-A00859–44).
2.3. Mobile Laboratory
The tests were carried out in a mobile hearing laboratory (
Figure 2 and
Figure 3), consisting of four audiometric booths. Each booth is equipped with experimental instruments that can be remotely controlled from the control room. The four booths were used simultaneously to optimize the experimental time for a group of participants. An audio and video system enabled communication between the experimenter and each of the four participants individually or simultaneously, and was used to remind them of the instructions, maintain motivation and monitor their state of arousal.
2.4. Speech Audiometry in Noise
Each participant performed three different speech audiometry in noise tests: 1. the consonant identification test, 2. the word recognition test, and 3. the French matrix test (FrMatrix). The masking provided by the noise was an energetic one (no informational masking was used). A closed set paradigm was used for the consonant identification test whereas an open set paradigm was used for the word recognition and the FrMatrix tests.
2.5. Consonant Identification Test
The consonant identification test consists of the presentation of 48 nonsense vowel-consonant-vowel-consonant-vowels (VCVCVs) spoken by a French female talker presented in a spectro-temporally modulated noise at -10 dB SNR. The signal was presented monaurally on the right ear. The 48 presentations came from three recordings of 16 French consonants (C= /p, t, k, b, d, g, f, s, ∫, v, z,ᶚ , l, m, n, r/), systematically associated with the vowel /a/. The duration of each presentation was on average 1272 ±113 ms.
For each trial, the participant had to indicate the perceived consonant by clicking on a matrix of 16 different consonants presented visually in front of them. No feedback was provided. The identification score corresponded to the percentage of correct answers. The presentation level was 65 dB SPL.
2.6. Words in Noise Recognition
Ten different lists were presented in the right ear of each participant. Each list consisted of 25 monosyllabic French words. Four different SNR ratios were compared: -5, 0, 5, 10 dB in a speech shaped noise and in silence. For each SNR, two lists (i.e. 50 words) were presented. The order of presentations was randomized and the association between lists and SNR conditions were counterbalanced. Each participant had to write, using the keyboard, the word he/she heard. Participants were instructed to write the words as they heard them, even if only one phoneme was heard, and to respect phoneme to grapheme conversion in the French language, not minding spelling mistakes. Each correspondence between the written word and target word from the list was then manually checked by 2 independent observers.
2.7. French Sentence Matrix Test
The French version of the sentence matrix test [
8] was used to determine the speech reception threshold of the participants. The sentences are all constructed using the same pattern: a firstname, a verb, a number, the name of an object and a color (for instance “Jean Luc ramène trois vélos rose”). There are 10 possible choices for each word categories. A test session consists of 20 sentences presented with an adaptative staircase procedure. The listener is seating one meter away facing the loudspeaker emitting the sentences and the noise. The participant’s task is to repeat aloud the words. The signal-to-noise ratio (fixed noise level at 65 dB SPL) varies from sentence to sentence depending on the number of correct words given by the participant in order to obtain the 50% speech reception threshold (SRT). Each participant performed three sessions. The final SRT of each participant (i.e. the dependent variable) is the best (i.e. lowest) value from the three sessions. The normative value is of -6 dB SNR (standard deviation 0.6 dB, [
9]).
2.8. Speech Spatial and Quality of Hearing Questionnaire
In addition to the above-described behavioral measures of speech intelligibility in noise, the participant also completed a self-report measure.
The Speech, Spatial and Qualities of Hearing Scale (SSQ) questionnaire enables measurement of a participant's ability in various listening situations (Gatehouse & Noble, 2004) using a numerical gradation from 0 (no, not at all) to 10 (yes, perfectly). The questionnaire is divided in three subscales: speech comprehension (14 questions), spatial hearing (17 questions) and hearing quality (18 questions).
The closer the numerical value is to 10, the more the subject feels able to perform the task
described. We used a French version of the questionnaire, previously validated (Moulin et al., 2015; Moulin & Richard, 2016). The average of items 1, 4 and 6 of the speech comprehension subscale were combined into the “speech in noise” pragmatic scale [
10].
2.9. Predictors of Speech in Noise Tests
In order to identify the best predictors of speech intelligibility in noise, several physiological and behavioral measurements were conducted. Altogether, when taking into account several markers (i.e. variables) for each measure type, a set of 48 variables was obtained. They are all described below. They were all expected to predict to a certain degree the speech-in-noise performance as measured by the four speech-in-noise tests described above (consonant identification, word recognition, FrMatrix and the speech in noise pragmatic scale of the SSQ).
2.9.1. Pure Tone Audiometry
The audiometric thresholds were recorded with an automatic procedure with the Echodia Elios® system (France) with Radiohear headphones DD45 at the left ear and at the right ear for the frequencies 125, 250, 500, 1000, 2000, 4000, 8000 and 12 500 Hz (
Figure 4). The 12500 Hz was defined as the EHF threshold. The four frequencies pure tone average (PTA; 500, 1000, 2000, 4000) were computed in both ears, and best ear PTA was identified as the lower PTA across the two ears. Therefore, nineteen predictor values were obtained from the pure tone audiometry.
2.9.2. Amplitude and Frequency Modulation Detection Thresholds
A total of eight thresholds were obtained for each participant using a two-interval forced-choice procedure from the combination of modulation type (AM or FM), sinusoidal carrier signal frequency (500 or 4000 Hz) and stimulus intensity (10 or 60 dB SL). The standard signal was unmodulated, i.e., the modulation depth (Δ) was set to 0. The target signal was modulated and the value of the modulation depth, Δ was adaptively modified in order to determine the threshold. All stimuli were generated digitally using a sampling rate of 44.1 kHz, and presented to participants at a presentation level of 10 dB SL or 60 dB SL, using Beyer DT 770 headphones and an external AudioEngine D3 sound card. Stimuli were presented monaurally to the right ear. Each trial consisted of a target modulated signal and a standard unmodulated signal, presented in random order, and separated by a 600 ms silence interval. The participant was instructed to indicate the stimulus containing the modulation, and was informed of the accuracy of his/her response by a light signal (green if correct, otherwise red). Each stimulus duration was 1200 ms.
Threshold was determined using a "2-down-1-up" method: Δf decreased when the participant responded correctly twice consecutively, and increased in the event of an error. The test stopped after 14 inversions, defined as an increase followed by a decrease in ∆ or vice versa. The detection threshold was calculated from the average ∆ over the last six inversions. Three threshold estimates were made for each intensity level condition (10 dB SL and 60 dB SL), and each type of modulation (AM and FM), and each carrier frequency (500 and 4000 Hz). The final value for each condition tested corresponded to the best performance obtained.
In our study, it appeared that some participants were not able to perform the task for three conditions of the FM detection task: 4000Hz carrier frequency at 10 dBSL; 4000 Hz carrier frequency at 60 dB SL; and 500 Hz carrier frequency at 10 dB SL. To take this into account, we created 3 two-levels categorical variables according to the ability of the participant to perform the task (able / not able). Therefore, 11 predictors were obtained from the AM and FM detection thresholds.
2.9.3. Distortion Products of Otoacoustic Emissions
Distorsion Products Otoacoustic Emissions (DPOAE) were collected with the Elios® system from Echodia (France). An f2/f1 ratio of 1.20 was used at intensity levels of f1=75 dB SPL and f2=65 dB SPL. The amplitude of DPOAE were recorded at frequencies of 1, 2, 3, 4 and 5 kHz at both ears (Recorded values of -10 dB SPL or lower were discarded) to obtain 10 predictors per participant.
2.9.4. Electrocochleography
The extratympanic electrocochleography (EcochG) was conducted with the Echodia Elios® system (France). Two electro-encephalogram electrodes were placed on the forehead of the participant (one centered and one off-center, the two on the hairline). The extratympanic electrode was a gold-coated soft Tiprode, positioned in the outer ear canal. The electric impedances of the electrodes were checked to be below 5 kΩ. Acoustic stimuli were short clicks delivered at a rate of 11/s. The recordings were collected at 90 dB nHL, then at 80 dB nHL. For each level, the procedure consisted of averaging 500 responses, repeated two or three times depending on the consistency of the waveforms across the 500 responses. For each waveform, the amplitude of the wave I was assessed by the difference in voltage between the first peak occurring between 1 and 2,5 ms and the next trough. Then, the amplitudes of the two most consistent waveforms were averaged. Furthermore, the slope of the input/output function obtained by linking the two stimulation levels (80 and 90 dB nHL) and the wave I amplitude was computed for each ear. Accordingly, six predictors (Wave I amplitude at 80 and 90 dB nHL at both ears plus Wave I slope at both ears) per participant were obtained.
2.9.5. Random Forest Analysis
In addition to the 48 predictor variables described above, 3 were added: the age; the number of years of motorcycling; and the history of hearing pathology (otitis media or acute acoustic trauma). 47 variables were continuous and 4 were categorical. The main goal here was to identify the most important predictors of speech-in-noise performance.
In order to perform this importance analysis, we used random forest algorithms. Recently, biomedical research in general has found an interest in this machine-learning paradigm, given its interpretability, its nonparametric approach with large use case, and the potential mix between continuous and categorical variables (REF). Random forests have already been identified as an interesting choice among machine learning algorithms in hearing sciences [
11,
12,
13].
A random forest is a combination of 500 decision trees. Each decision tree is built from a random sample of the population and a random sample of the variables to reduce the risk of overfitting. Next, all 500 trees are combined to build a model and make a prediction. A prediction error is computed from the data excluded from the random samples (“error out of the box”). The difference between the observed value to predict and the actual prediction is represented by the mean square error (MSE). To assess the importance of a variable, the impact of random permutations of that variable is measured on the MSE. The more the MSE increases, the more important the variable is.
In order to have a reliable measure of each variable importance, the non-scaled importance measure was computed on 10 subsamples and then averaged across samples. The subsamples were built by randomly selecting 75% of the original data sample.
We used the randomForest R package Version: 4.7-1.1 with the hyperparameters set by default. Missing values were handled by the command “na.action==na.roughfix”.
In addition to the importance graph for each speech-in-noise test, we described the 9 most important variables and the correlations between the speech-in-noise performance and the variable (predictor). Hence, nine scatterplots graphs are plotted for each speech-in-noise audiometry. On each scatterplot, the Spearman coefficient of correlation, its p-value and the size sample are indicated. Spearman coefficient was chosen against Pearson coefficient because several variables were not normally distributed (e.g. speech in noise pragmatic scale, years of motorcycling), and for consistency with the nonparametric algorithm of the random forest.
2.9.6. Missing Values
The global sample size was 70 participants. However, due to various obstacles encountered during the experiment (mainly professional availability; hardware malfunctions; inability to record some of the measure for some participants, see explanations above), the sample size for each variable was less than 70 (see
Table 1 for details).
Author Contributions
Conceptualization, GA, CS, NP; methodology, GA, CS, NP, AM, FG, NW, VI.; software, NW, VI.; validation, GA.; formal analysis, GA, VI.; investigation, GA, CS, FG.; resources, GA, AM, FG, NW; data curation, GA., AM; writing—original draft preparation, GA, CS; writing—review and editing, GA, CS, NP, AM, FG, VI; visualization, GA, VI ; supervision, GA.; project administration, GA. All authors have read and agreed to the published version of the manuscript.
Figure 1.
Distribution of the age of participants. The boxplots show the medial (horizontal bar) and the interquartile range (box). The whiskers reach from the lowest to the highest observed value within 1.5 times the interquartile range. Each dot show the age of one participant.
Figure 1.
Distribution of the age of participants. The boxplots show the medial (horizontal bar) and the interquartile range (box). The whiskers reach from the lowest to the highest observed value within 1.5 times the interquartile range. Each dot show the age of one participant.
Figure 2.
Exterior view of the Mobile Hearing Laboratory.
Figure 2.
Exterior view of the Mobile Hearing Laboratory.
Figure 3.
Interior view of the Mobile Hearing Laboratory. In the center right of the setup, a video screen displays images of participants situated in the four booths. Positioned in the center left are four portable "followers" computers equipped with fold-down screens, to which the screens, keyboards, and mice of each booth are connected. Beneath these computers, there is the "leader" computer, positioned at the bottom center, with its screen visible. Additionally, the screens corresponding to the "follower" computers are also visible.
Figure 3.
Interior view of the Mobile Hearing Laboratory. In the center right of the setup, a video screen displays images of participants situated in the four booths. Positioned in the center left are four portable "followers" computers equipped with fold-down screens, to which the screens, keyboards, and mice of each booth are connected. Beneath these computers, there is the "leader" computer, positioned at the bottom center, with its screen visible. Additionally, the screens corresponding to the "follower" computers are also visible.
Figure 4.
Audiometric thresholds as a function of frequency for left and right ear (N = 70). The black line shows the median, the grey area show the interquartile range.
Figure 4.
Audiometric thresholds as a function of frequency for left and right ear (N = 70). The black line shows the median, the grey area show the interquartile range.
Figure 5.
The performance for each speech audiometry in noise; each dot show the result of one participant. The boxplots show the medial (horizontal bar) and the interquartile range (box). The whiskers reach from the lowest to the highest observed value within 1.5 times the interquartile range.
Figure 5.
The performance for each speech audiometry in noise; each dot show the result of one participant. The boxplots show the medial (horizontal bar) and the interquartile range (box). The whiskers reach from the lowest to the highest observed value within 1.5 times the interquartile range.
Figure 6.
Main predictors of the consonant Identification score. The importance is measured as the MSE increase for the nine first most important variables. The larger the value, the more important the variable. See
Table 1 for abbreviations.
Figure 6.
Main predictors of the consonant Identification score. The importance is measured as the MSE increase for the nine first most important variables. The larger the value, the more important the variable. See
Table 1 for abbreviations.
Figure 7.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Right ear EHF Threshold. B. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. C. Left Ear 8000Hz Threshold. D. Left Ear Pure Tone Average. E. Left Ear Wave I Amplitude at 80 dB nHL. F. Years of Motorcycling. G. Best Ear Pure Tone Average. H. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz. I. Left ear EHF Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 7.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Right ear EHF Threshold. B. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. C. Left Ear 8000Hz Threshold. D. Left Ear Pure Tone Average. E. Left Ear Wave I Amplitude at 80 dB nHL. F. Years of Motorcycling. G. Best Ear Pure Tone Average. H. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz. I. Left ear EHF Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 8.
Word in Noise Recognition. Importance measured as the increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model. See
Table 1 for abbreviations.
Figure 8.
Word in Noise Recognition. Importance measured as the increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model. See
Table 1 for abbreviations.
Figure 9.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz B. Right ear EHF Threshold. C. History of Hearing Pathology. D. Left Ear 1000 Hz Threshold. Pure Tone Average. E. Left Ear EHF Threshold. F. Years of Motorcycling. G. Left Ear DPOAE at 2000 Hz. H. Left Ear DPOAE at 1000 Hz. I. Left Ear 2000 Hz Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 9.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz B. Right ear EHF Threshold. C. History of Hearing Pathology. D. Left Ear 1000 Hz Threshold. Pure Tone Average. E. Left Ear EHF Threshold. F. Years of Motorcycling. G. Left Ear DPOAE at 2000 Hz. H. Left Ear DPOAE at 1000 Hz. I. Left Ear 2000 Hz Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 10.
French Matrix test. Importance measured as the Increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model.
Figure 10.
French Matrix test. Importance measured as the Increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model.
Figure 11.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Right ear EHF Threshold. B. Years of Motorcycling. C. Right Ear DPOAE at 3000 Hz. D. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. E. History of Hearing Pathology. F. Right Ear 4000 Hz Threshold. G. Right Ear 125 Hz Threshold. H. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. I. Left Ear EHF Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 11.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Right ear EHF Threshold. B. Years of Motorcycling. C. Right Ear DPOAE at 3000 Hz. D. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. E. History of Hearing Pathology. F. Right Ear 4000 Hz Threshold. G. Right Ear 125 Hz Threshold. H. Amplitude Modulation Detection Threshold at 60 dB SL at 500 Hz. I. Left Ear EHF Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 12.
Speech in Noise Pragmatic Scale. Importance measured as the Increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model.
Figure 12.
Speech in Noise Pragmatic Scale. Importance measured as the Increase of the mean square error for the nine most important variables. The larger the value is, the more important the variable in the model.
Figure 13.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Years of Motorcycling. B. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz. C. Right Ear Pure Tone Average. D. Best Ear Pure Tone Average. E. Right Ear 8000 Hz Threshold. F. Left Ear EHF Threshold. G. Left Ear DPOAE at 3000 Hz. H. Age. I. Right Ear 1000 Hz Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 13.
Scatter plots of the linear regression of the nine most important predictors of the consonant identification score. A. Years of Motorcycling. B. Frequency Modulation Detection Threshold at 60 dB SL at 500 Hz. C. Right Ear Pure Tone Average. D. Best Ear Pure Tone Average. E. Right Ear 8000 Hz Threshold. F. Left Ear EHF Threshold. G. Left Ear DPOAE at 3000 Hz. H. Age. I. Right Ear 1000 Hz Threshold. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 14.
Scatter plots showing the correlations between the three speech audiometries in noise: A. Consonant Identification vs. French Matrix Test. B. Words in Noise Recognition vs. French Matrix Test. C. Consonant Identification vs Words in Noise Recognition. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 14.
Scatter plots showing the correlations between the three speech audiometries in noise: A. Consonant Identification vs. French Matrix Test. B. Words in Noise Recognition vs. French Matrix Test. C. Consonant Identification vs Words in Noise Recognition. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 15.
Scatter plots showing the correlations between the speech in noise pragmatic scale and the three speech audiometries in noise. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Figure 15.
Scatter plots showing the correlations between the speech in noise pragmatic scale and the three speech audiometries in noise. The blue line indicates the linear fit. The gray region indicates the 95% confidence interval of the regression line. In each panel, the Spearman coefficient of correlation, its p-value and the sample size are shown.
Table 1.
Sample size of each combination of variable and conditions of the dataset. AMDT: Detection threshold of AM, FMDT: Detection threshold of FM, DPOAE: Distortion Products of OtoAcoustic Emission.
Table 1.
Sample size of each combination of variable and conditions of the dataset. AMDT: Detection threshold of AM, FMDT: Detection threshold of FM, DPOAE: Distortion Products of OtoAcoustic Emission.
Test |
Conditions |
N |
Abbreviation |
Consonant Identification |
|
61 |
|
Word in Noise Recognition |
|
56 |
|
French Matrix Test |
|
69 |
FrMatrix |
Age |
|
70 |
Age |
History of Hearing Pathology |
|
70 |
History_of_Hearing_Pathology |
Years of Motocycling |
|
70 |
Years_of_Motocycling |
AMDT |
60 dB SL 4000 Hz |
42 |
AMDT_60dB_4000Hz |
60 dB SL 500 Hz |
55 |
AMDT_60dB_500Hz |
10 dB SL 4000 Hz |
55 |
AMDT_10dB_4000Hz |
10 dB SL 500 Hz |
55 |
AMDT_10dB_500Hz |
FMDT |
60 dB SL 4000 Hz |
22 |
FMDT_60dB_4000Hz |
60 dB SL 500 Hz |
61 |
FMDT_60dB_500Hz |
10 dB SL 4000 Hz |
23 |
FMDT_10dB_4000Hz |
10 dB SL 500 Hz |
45 |
FMDT_10dB_500Hz |
60 dB SL 4000 Hz Ability |
60 |
FMDT_60dB_4000Hz_Ab |
10 dB SL 4000 Hz Ability |
61 |
FMDT_10dB_4000Hz_Ab |
10 dB SL 500 Hz Ability |
61 |
FMDT_10dB_500Hz_Ab |
DPOAE |
Left Ear 1000 Hz |
59 |
LE_DPOAE_1000Hz |
Left Ear 1500 Hz |
62 |
LE_DPOAE_1500Hz |
Left Ear 2000 Hz |
62 |
LE_DPOAE_2000Hz |
Left Ear 3000 Hz |
62 |
LE_DPOAE_3000Hz |
Left Ear 4000 Hz |
62 |
LE_DPOAE_4000Hz |
Left Ear 5000 Hz |
58 |
LE_DPOAE_5000Hz |
Right Ear 1000 Hz |
65 |
RE_DPOAE_1000Hz |
Right Ear 1500 Hz |
63 |
RE_DPOAE_1500Hz |
Right Ear 2000 Hz |
65 |
RE_DPOAE_2000Hz |
Right Ear 3000 Hz |
65 |
RE_DPOAE_3000Hz |
Right Ear 4000 Hz |
65 |
RE_DPOAE_4000Hz |
Right Ear 5000 Hz |
61 |
RE_DPOAE_5000Hz |
Tonal Audiometry |
Left Ear 125 Hz |
70 |
LE_125Hz |
Left Ear 250 Hz |
70 |
LE_250Hz |
Left Ear 500 Hz |
70 |
LE_500Hz |
Left Ear 1000 Hz |
70 |
LE_1000Hz |
Left Ear 2000 Hz |
70 |
LE_2000Hz |
Left Ear 4000 Hz |
70 |
LE_4000Hz |
Left Ear 8000 Hz |
70 |
LE_8000Hz |
Left Ear EHF |
70 |
LE_EHF |
Left Ear PTA |
70 |
LE_PTA |
Right Ear 125 Hz |
70 |
RE_125Hz |
Right Ear 250 Hz |
70 |
RE_250Hz |
Right Ear 500 Hz |
70 |
RE_500Hz |
|
Right Ear 1000 Hz |
70 |
RE_1000Hz |
|
Right Ear 2000 Hz |
70 |
RE_2000Hz |
|
Right Ear 4000 Hz |
70 |
RE_4000Hz |
|
Right Ear 8000 Hz |
70 |
RE_8000Hz |
|
Right Ear EHF |
70 |
RE_EHF |
|
Right Ear PTA |
70 |
RE_PTA |
|
Best Ear PTA |
70 |
Best_Ear_PTA |
Electrocochleography |
Left Ear Wave I 80 dB HL |
37 |
LE_WaveI_80dB |
Left Ear Wave I 90 dB HL |
38 |
LE_WaveI_90dB |
Right Ear Wave I 80 dB HL |
49 |
RE_WaveI_80dB |
Right Ear Wave I 90 dB HL |
52 |
LE_WaveI_90dB |
Left Ear Wave I Slope |
34 |
LE_Slope |
Right Ear Wave I Slope |
46 |
RE_Slope |