More than 5% of the world’s population, approximately 460 million people, suffer from disabling hearing loss (WHO, 2021). Hearing disability is associated with reduced speech perception and, in consequence, reduced communication function. Hearing deteriorates with age (Zwaardemaker, 1891; Technical Committee ISO/TC 43 Acoustics, 2017). The ISO standard describes the age-dependent frequency-specific loss (ISO 7029:2017, 2017). The slope of the decline increases with growing age and frequency: While for 250 Hz the decline is in the order of 1 dB per decade in the fourth life decade, about 20 dB per decade can be observed for 6,000 Hz in the eighth life decade. While the ISO standard provides detailed information about the relationship between age and pure-tone sensitivity loss (PTSL), it makes no reference to speech recognition.

Given our ageing society and the prevalence of age-related hearing loss (ARHL), it is clear that hearing loss is a common public-health issue of increasing importance in the near future (WHO, 2021). Individuals with ARHL experience social withdrawal (Pronk et al., 2011), mental and physical decline (Shukla et al., 2020), and poorer quality of life (Davis et al., 2007).

Speech perception deficits in hearing-impaired people are mainly attributable to decreased audibility of the speech signal over part or all of the speech frequency range. Within Carhart’s (1951) framework for word recognition in quiet, this was referred to as loss of acuity. Additionally, Carhart introduced a second component which stems from impaired processing of the audible speech signal, resulting in a loss of clarity. Plomp (1978) referred to these components of hearing loss as attenuation (class A) and distortion (class D), respectively. The attenuation component can be assessed by pure-tone audiometry. The distortion component describes the impact of reduced temporal and frequency resolution. It is thought that the distortion component explains the deterioration of speech recognition which is not described by attenuation, namely pure-tone thresholds. Both attenuation and distortion are part of ARHL (van Rooij et al., 1989).

A large number of studies have focussed upon hearing in the elderly and have investigated PTSL and speech perception. However, the interpretation of these results remains challenging, as pure-tone thresholds change substantially with increasing age. Hence, it is necessary to correct for the effect of PTSL when investigating the effect of age on speech perception. One of the first attempts to do this was described by Jerger (1973) in a report on speech recognition in a large group of older subjects. He analysed scores from the clinical records of 2,162 patients. With subjects grouped according to age and average hearing loss at 0.5, 1 and 2 kHz, results suggested that speech recognition, defined as the maximum score (WRS_max) obtained by using a monosyllabic word list, declines above the age of sixty. In particular, he found that age had an effect on speech recognition of approximately 4% per life decade for individuals with mild hearing loss, but that it had a greater effect (e.g., 10% per decade) upon those with higher degrees of hearing loss. Unfortunately, he did not report on hearing loss at higher frequencies. It is known for a long time that hearing thresholds at these higher frequencies are, in particular, worse for older subjects (Zwaardemaker, 1891; Technical Committee ISO/TC 43 Acoustics, 2017).

Several studies have revealed that deterioration in speech understanding occurs in addition to deterioration in hearing sensitivity and includes components beyond elevated hearing thresholds (Bergman et al., 1976; Jerger and Hayes, 1977; Marshall and Bacon, 1981; Pedersen et al., 1991; Divenyi and Haupt, 1997; Kronlachner et al., 2018).

Some authors (Dubno et al., 1997; Humes, 2007) have highlighted the challenge of separating varying auditory thresholds from age, a factor affecting all sensory modalities (Humes and Young, 2016). In recent studies, speech recognition and its relation to age were investigated either by correcting for PTSL (Hoppe et al., 2014; Müller et al., 2016) or by using a longitudinal study design (Dubno et al., 2008). In a clinical population Hoppe et al. (2014) investigated speech recognition with hearing aids and WRS_max for different age groups in relation to average hearing loss at 0.5, 1, 2, and 4 kHz (4FPTA). They found a monotonic decrease in speech recognition with increasing age and a significant drop of about 2–4% per decade. This drop was attributed to age-dependent distortion. Müller et al. (2016) investigated, as well, the WRS_max as a function of age. After correcting for 4FPTA they found a significant, though smaller, drop for people aged above 70 years of about 2–3% per decade. Neither study included a hearing threshold beyond 4 kHz, and therefore, a small overestimation of the influence of age cannot be excluded. However, Dubno et al. (2008) found a larger effect, around 7–8% per life decade. They performed a longitudinal study including 256 subjects with age-related hearing loss, aged 50–82 years, over a period of 3–15 years. The speech recognition scores were corrected for by changing hearing thresholds during the observation phase; this was done by using the individuals’ articulation index as an importance-weighted metric for speech audibility. Unfortunately, longitudinal studies suffer from other disadvantages relating to population size, loss of follow-up etc., and their duration can approach the limits of the clinician’s working life span. The special characteristics of the study population and methods—neither the WRS_max nor hearing-aid scores were measured—differ from the studies mentioned above. This impedes a direct comparison with the above-mentioned studies and therefore does not imply a contradiction amongst them.

In summary, increased PTSL is the most common expression of ARHL. However, there is evidence that a number of other auditory functions are affected as well (Profant et al., 2019). These functions decline with increasing age and the PTSL does not predict speech recognition sufficiently well.

The goal of this study is to describe the relationship between hearing loss, age, and speech recognition by means of a machine-learning algorithm (Random Forest Regression, RFR, Breiman, 2001). RFR is an algorithm that uses an ensemble method of decision-tree-based regressions to determine a response from a set of input variables. It does not rely on any particular assumptions regarding data distribution. This algorithm is applied to a large data set from routine clinical audiometry in order to investigate the influence of age. The result is a representation of the relationship between pure-tone thresholds and age on the input side and speech recognition on the target side. The model reflects the influence of the age-related distortion component on speech perception.

Additionally, the results of the RFR model will be compared with those of a generalised linear model (GLM) approach. In contrast to the RFR, the GLM requires assumptions about the qualitative relation between input and target variables, whereas the RFR does not need a pre-defined equation framework.

In order to categorise pure-tone thresholds, standard audiograms as proposed by Bisgaard et al. (2010) are used as model input. Both derived models (the RFR and GLM) will be applied to these standard audiograms.

Audiometric data were retrieved from a clinical data base at the Audiological Department of Erlangen University Hospital. From the routine audiometric measurements, pure-tone thresholds for both bone and air conduction were extracted. Additionally, speech recognition scores for monosyllabic word lists of 20 items for each presentation level of the Freiburg Test (Hahlbrock, 1957) were evaluated. The complete discrimination function, ranging from 65 dB_SPL up to 120 dB_SPL was measured. All measurements had been conducted in clinical routine in sound-shielded booths with clinical class A audiometers (AT900/AT1000 AURITEC Medizindiagnostische Systeme GmbH, Hamburg, Germany). Approval for this study was received from the Institutional Review Board of the University of Erlangen (Ref. No. 162_17 Bc). All methods were carried out in accordance with relevant guidelines and regulations.

Figures 2, 3 depict the basic characteristics of the clinical population investigated. The stacked bar plot (Figure 2) shows the case distribution in our clinical population (N = 19,801). The mean ages of the different groups were 50, 61, 66, 65, and 59 years for WHO₀, WHO₁, WHO₂, WHO₃ and WHO₄. The vast majority (77%) of cases involved persons between 40 and 80 years of age. The subjects aged 40–80 years dominated all WHO grades except WHO₀. The smallest data coverage with respect to age and hearing loss was observed for very young adults in the WHO₄ group and for subjects above 80 years of age in the WHO₀ group.

The speech audiometric results for the model’s target scores, WRS₆₅ and WRS_max, are shown in Figures 3A,B, respectively. For both measures the median decreased with increasing degrees of hearing loss. The Kruskal–Wallis Test yielded significant group effects for WRS₆₅ (χ² = 15.055, p < 10^–15, df = 4) WRS₆₅ and WRS_max (χ² = 11.873, p < 10^–15, df = 4). The interquartile ranges for WRS₆₅ were 5, 25, 50, 0, and 0% for WHO₀, WHO₁, WHO₂, WHO₃, and WHO₄, respectively. The interquartile ranges for WRS_max were 0, 0, 25, 40, and 30% for the corresponding WHO groups. The variability for WRS₆₅ was largest for WHO₁, while for WRS_max the largest variability was found for WHO₃. In this rather rough classification the interpretation of some outliers may benefit from additional information about the specific configuration of hearing loss. In particular, the WHO classification employs the hearing thresholds at only four frequencies, while other frequencies are not considered. The lowest quartile of the WHO₀ cases shows a WRS₆₅ lower than 95%. In this subgroup the mean threshold for high frequencies (>4 kHz) was 48 dB_HL, while for the cases with WRS₆₅ above 95% in the WHO₀ group the mean threshold for high frequencies was 25 dB_HL in the WHO₀ group.

The analysis of a large clinical database allows the description of the age-related decline of speech perception in detail. In comparison with previous studies, more detailed information about the time course and amount of degradation was achieved by means of RFR. Both models, the GLM and the RFR, describe an age-related decline in speech recognition after being corrected for PTSL. The GLM is based on predefined hypotheses and confirms significant age effects. Inevitably, the relationship between age and speech scores follows the underlying functional relations. The GLM results in an age-related decline for WRS_max of about 3–4% per decade for N₄-N₆, and S₃. For all other audiogram types smaller effects were found owing to saturation effects. This is in concordance with previous studies (Jerger, 1973; Dubno et al., 2008). WRS₆₅ decreases at a rate of up to 2.5% per decade for mild hearing losses, i.e., N_2/3 and S₂. For the other audiogram types the GLM yielded smaller rates of decline. Owing to the lower presentation level of 65 dB_SPL floor effects were observed even for moderate hearing losses, i.e., N_4–7. The RFR model yielded more specific information about the time course and rate of decline. Additionally, the RFR model allows the quantitative description of the two basic effects of hearing loss and its relation to age: On the one hand the impact of the attenuation component of ARHL, and on the other hand the impact of the distortion component of ARHL. This could be achieved by keeping constant the model input variables representing PTSL (attenuation), and by modifying the model input variable representing age. It therefore offers the opportunity to overcome a bias that was immanent in previous investigations (Jerger, 1973; Marshall and Bacon, 1981; Dubno et al., 1997; Hoppe et al., 2014; Müller et al., 2016) by isolating age-related hearing threshold elevation from age-related decline in speech recognition as such.

This study should not be misunderstood as an attempt to predict speech recognition scores on the basis of PTSL. These scores have to be measured individually. The large variability of individual scores necessitates speech audiometry. The purpose of the model in this study was to analyse the impact of age for larger patient populations with respect to specific audiogram types. It can be seen in Figure 4 that those age-related changes are present for the entire duration of adulthood. However, apart from the fact that higher age relates to lower speech recognition scores, no common quantitative trend, for any age groups or PTSL, can be discerned. This may be regarded as the major outcome of the RFR model calculations. The measurable age-related decline in speech recognition depends on the age range considered, the specific audiogram, and the specific application of speech audiometry. Owing to saturation effects of the WRS₆₅ measured at typical conversation level, we observed the largest age effect for moderate hearing losses (N₃-type audiograms). For the WRS_max measured at substantially higher levels, the largest effects were observed for audiogram types corresponding to severe hearing losses (N₄, N₅, N₆). This result of the RFR is in agreement with findings of Jerger (1973). Even though the variability in his data is considerable (as in our data) one may conclude that a stronger age-related decline can be observed for later life decades and greater hearing loss. Additionally, Jerger’s data also indicated that the onset of age-related decline may occur already at younger age. This is in line with our results where the RFR model e.g., yielded for N₆ the strongest decline for WRS_max of 20% per decade around the fifth and sixth life decade.

According to the RFR, the decrease in the WRS_max was counterbalanced by an increased presentation level for all audiogram types except N₆. The N₆ -type audiogram showed the largest age-related decline in speech recognition. The decreased tolerance of higher presentation levels may have contributed to this decline. This might reflect certain underlying pathomechanisms that are more likely to be present in patients with this audiogram type compared with others. Complementary to attenuation and distortion, a causal and more differentiated breakdown with respect to presbyacusis was proposed early on. Finally, five main types were proposed, namely sensory, neural, metabolic, mechanical, and vascular presbyacusis (Schuknecht, 1964; Johnsson and Hawkins, 1972). This was complemented by the term central presbyacusis in order to reserve the term neural for degeneration of the cochlear nerve. Sensory presbyacusis is congruent with the attenuation component and is, as pointed out above, represented by the audiogram type as a fixed parameter in Figure 4. The effects of all the other types of presbyacusis are included in the specific relationships between age and WRSs, respectively, L_max. Moreover, the specific and different root causes may potentially explain why, for some degrees of hearing loss, different changes in speech perception occur in different life decades. However, possible interactions between—or even independent mechanisms—of the main types of presbyacusis are still not completely understood (Bao and Ohlemiller, 2010; Profant et al., 2019).

It is not possible to confirm all these explanatory hypotheses by retrospective data analyses, a fact that clearly underlines the limits of our study design. We found differences in age effects in comparison with some of the studies referred to above. This is partly due to the neglect of hearing loss at higher frequencies for the elderly in those studies. On the other hand, for some hearing losses and audiogram types, this study may underestimate age effects, as ceiling effects of speech tests in quiet are included. Another aspect of this study is the inclusion of a considerable number of subjects with mild hearing loss, as seen in group S₁. Even in that group, age effects play a part. Especially the WRS₆₅ illustrates how everyday communicative ability in quiet might be already affected by mild to moderate hearing loss in a population in which the use of hearing aids does not reach the penetration level needed (Halpin and Rauch, 2012).

Other possible applications of the RFR model are related to acoustic amplification with hearing aids: As shown in Figure 4, in all groups except N₆, the level for best speech recognition (L_max) increases with age at about 0.5 dB per decade. This may indicate that older people may benefit from larger sound pressure levels for speech recognition, i.e., greater amplification, when provided with a hearing aid. As far as we know, current amplification strategies do not take this into account. On the other hand, one has to consider that in some pathologies more amplification might be detrimental rather than beneficial (Halpin and Rauch, 2012).

The age dependence of the WRS_max found in our study may be used to improve studies evaluating the outcome of hearing aid use: The WRS_max or an equivalent measure is often used as reference for the measurement of successful hearing aid provision or other acoustic amplification (Halpin and Rauch, 2012; Hoppe et al., 2014; Müller A. et al., 2017; Maier et al., 2018a,b), for investigation of age-related changes in cognition (Kronlachner et al., 2018), and for speech-perception-related studies in general (Müller J. et al., 2017). A consideration of both age and specific audiogram type could potentially decrease the variability of results. Furthermore, the functional relation between audiogram types and speech perception as presented here can be used to link epidemiological studies on hearing loss (Sohn and Jörgenshaus, 2001; von Gablenz et al., 2017, 2020; Chang et al., 2019; Löhler et al., 2019; Cantuaria et al., 2021) with speech recognition.

A random-forest regression model allowed the estimation of age-related decline of speech recognition in quiet, completely separated from the effect of pure-tone sensitivity loss. Noticeable declines were found across the whole duration of adulthood and for all audiogram types. Model calculations resulted in a decrease of up to 25 percentage points word recognition scores across the whole life span. Depending on the specific hearing loss, the RFR model indicated a maximum decline of up to 10 percentage points in certain life decades. The decline can be attributed to an increased distortion component related to presbyacusis which is not represented by pure-tone audiometry. The careful derivation of working hypotheses from our data has the potential to provide greater insight into the relationships between pure-tone sensitivity loss, specific audiogram types and age.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by Ethik-Kommission, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The patients/participants provided their written informed consent to participate in this study.

UH: conceptualization. UH and TH: formal analysis, writing original draft, methodology, software, validation, and visualization. UH and HI: investigation, project administration, and resources. UH, TH, and HI: writing – review and editing. All authors contributed to the article and approved the submitted version.

TH was employed by Cochlear Deutschland GmbH & Co. KG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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