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Abstract

Speech audiometry in noise based on sentence tests is an important diagnostic tool to assess listeners’ speech recognition threshold (SRT), i.e., the signal-to-noise ratio corresponding to 50% intelligibility. The clinical standard measurement procedure requires a professional experimenter to record and evaluate the response (expert-conducted speech audiometry). The use of automatic speech recognition enables self-conducted measurements with an easy-to-use speech-based interface. This article compares self-conducted SRT measurements using smart speakers with expert-conducted laboratory measurements. With smart speakers, there is no control over the absolute presentation level, potential errors from the automated response logging, and room acoustics. We investigate the differences between highly controlled measurements in the laboratory and smart speaker-based tests for young normal-hearing (NH) listeners as well as for elderly NH, mildly and moderately hearing-impaired listeners in low, medium, and highly reverberant room acoustics. For the smart speaker setup, we observe an overall bias in the SRT result that depends on the hearing loss. The bias ranges from +0.7 dB for elderly moderately hearing-impaired listeners to +2.2 dB for young NH listeners. The intrasubject standard deviation is close to the clinical standard deviation (0.57/0.69 dB for the young/elderly NH compared with 0.5 dB observed for clinical tests and 0.93/1.09 dB for the mild/moderate hearing-impaired listeners compared with 0.9 dB). For detecting a clinically elevated SRT, the speech-based test achieves an area under the curve value of 0.95 and therefore seems promising for complementing clinical measurements.

Keywords

speech audiometry smart home matrix sentence test hearing screening

Speech intelligibility, especially in noisy conditions, is a crucial factor of successful social interaction and is often limited for hearing-impaired (HI) listeners, which potentially reduces their quality of life. Early supply with hearing aids can ease this limitation (Arlinger, 2003) but requires an early and reliable diagnosis of hearing loss. However, hearing aid adoption rates are low, especially for mild-to-moderate hearing loss, and typically 1 to 7 years pass between becoming aware of a hearing loss and being provided with hearing aids (EuroTrack Germany 2018).

A reliable measurement tool with a high accuracy for quantifying speech intelligibility in noise is internationally available through matrix sentence tests (Kollmeier et al., 2015). Due to the closed-vocabulary construction of this test with a vocabulary size of 50 words, it allows for an unsupervised measurement with a graphical user interface (the so-called closed response format). Nevertheless, a graphical interface excludes subjects who cannot read, such as children, visually impaired, and illiterate people. As an example, 12% of the population aged between 18 and 64 years in Germany is functionally illiterate (Grotlüschen et al., 2019). Furthermore, finding the correct words in a 50-word matrix might increase the effort for the subject to conduct the measurement, which could increase the influence of cognitive skills on the result. For elderly subjects, it is often not possible to conduct the measurement with a graphical user interface in a reasonable time (Brand et al., 2004; Brand & Wagener, 2017).

To overcome these limitations, we explore systems based on human–machine interaction using a speech interface. A similar approach was used for the automatically conducting the Dutch LIST sentence test with the goal of quantifying intelligibility of patients with cochlear implants (Deprez et al., 2013). In our own previous work, we developed a speech-controlled automated matrix (SAMT) test that uses automatic speech recognition (ASR) for response logging. The system has been evaluated in a clinical setting with well-controlled acoustics settings in the laboratory (Ooster et al., 2018): This system was built to be used in a sound-isolated hearing booth where it is possible to record clean audio from the subjects’ responses. With an ASR system fine-tuned for the vocabulary of the matrix sentence test, very low error rates below 1% deletion errors and 3% insertion errors could be achieved using responses from 20 normal-hearing (NH) subjects and 7 mildly HI subjects. With such low error rates, the measurement reliability using ASR was not significantly different from the clinical test. Furthermore, detailed Monte–Carlo simulations of the measurement procedure and potential ASR errors showed that up to 7% deletion errors and 15% insertion are acceptable for an accurate measurement (i.e., in the range of the normal test–retest accuracy of the test when it is conducted by a human supervisor).

Speech-controlled automated matrix test uses an ASR-based setup for clinical environments, but it has not been designed for use at home. One example of a speech-in-noise test which already has been successfully implemented for screening purposes via telephone or headphones is the digit triplet test (De Sousa et al., 2020; Smits et al., 2006, 2013; Vlaming et al., 2011; Zokoll et al., 2012), which is also available as a smartphone-based measurement (Potgieter et al., 2016). The limitation to digits enables automated telephone testing since the subjects’ responses can be logged using the keypad of the telephone. However, it also limits the ecological validity of the test results as the words are not phonetically balanced and the linguistic variety is small.

Smart speakers, i.e., voice-controlled audio devices connected to a virtual assistant such as Amazon’s Echo, Apple’s HomePod, or Google Home also have the potential of increasing the accessibility of speech intelligibility tests by performing self-measurements at home, since they provide a good audio quality and have a built-in dialogue management system including an ASR component. There have been several approaches to use smart home systems for medical purposes, for example, to provide acoustic cues to support dementia patients’ memory (Boumpa et al., 2019) or to support elderly people in their physical therapy (Vora et al., 2017). The Apple ResearchKit (Apple Inc., 2016) features a speech-in-noise test that is similar to our approach but has not been compared with a standard audiological test in a clinical setting.

In this article, we present a smart speaker application for measuring the speech recognition threshold (SRT), i.e., the signal-to-noise ratio corresponding to 50% intelligibility, with the matrix sentence test.¹ Due to the increasing availability of smart speakers, an accurate screening procedure for hearing deficits could potentially lower the threshold for conducting tests for a large number of users and therefore have a positive effect on early provision of assistive hearing devices. In a previous pilot study, the smart speaker-based measurement was evaluated in a single office room with six young, NH listeners, where a similar performance to clinical laboratory results was found (Ooster et al., 2019). However, the reliability for HI subjects was not part of the previous study although this aspect is crucial for speech audiometry. Furthermore, in a real use case, the acoustic conditions in which the test is conducted can exhibit large variability which could also influence reliability.

To explore automated at-home hearing screening for such use cases, this study therefore compares clinical SRT measurements with SRTs obtained with a smart speaker application. Specifically, we analyze the errors and the resulting measurement reliability from the ASR system of a smart speaker in comparison to a calibrated clinical setup conducted by an expert; we also investigate potential decision thresholds for providing simple feedback to the user. These analyses are conducted for users with different degrees of hearing loss (HL) (ranging from young normal hearing to elderly, moderately HI listeners), performing the test in different acoustic conditions with the aim of quantifying the interaction of test accuracy, user group, and environment. Room acoustics are taken into account by simulating three different acoustic environments with different reverberation times which is realized through a room acoustic simulator.

Methods

This section presents an overview of the underlying principles of the matrix sentence test that are used in the smart speaker measurement as well as in the clinical reference measurement. We describe the implementation of this test as an application for the smart speaker and conclude with a description of the evaluation measurements as well as the data analysis performed on the measurement results.

Matrix Sentence Test

The speech audiometric test used in this study is the German matrix sentence test OLSA (short for its German name: OLdenburger SAtztest, English: Oldenburg sentence test; Wagener et al., 1999). The words of the stimulus sentences are randomly selected from a 5-by-10 word matrix in order to create sentences with the structure Name Verb Numeral Adjective Object. The stimulus material is arranged in lists of sentences with the aim of providing phonetically balanced listening tasks with similar intelligibility. This design choice results in a low test-to-retest standard deviation of 0.9 dB for HI subjects (Wagener & Brand, 2005) and 0.5 dB for NH subjects (Brand & Kollmeier, 2002). The NH reference values are $(- 7.1 \pm 1.1)$ dB with the male stimulus speaker (Wagener et al., 1999) and $(- 9.4 \pm 1.0)$ dB with the female stimulus speaker (Wagener et al., 2014). The influence of a hearing loss on the SRT value measured with the matrix sentence test can be found in Wardenga et al. (2015). The standardization of how to construct, record, and optimize the test across languages (Akeroyd et al., 2015) also yields a high comparability across different languages (Kollmeier et al., 2015; Zokoll et al., 2013). During testing, the subject repeats the words he or she has recognized from the noisy sentence. Based on this response, the number of correctly recognized words N (referred to as sentence score) is calculated. Since the target value is the SRT, the signal-to-noise ratio (SNR) is dynamically adapted: It is increased for a word error rate below 50% and decreased otherwise (Brand & Kollmeier, 2002). The final measurement outcome is estimated by a maximum likelihood fit to all the data points from the complete measurement list with 20 sentences where the underlying data distribution is given by a psychometric function, i.e., a logistic sigmoid.

The Smart Speaker Application

An overview of the elements of the smart speaker application for the automated SRT measurement is shown in Figure 1 (adopted from Ooster et al., 2019). The setup differs from established clinical setups in several ways: (a) It uses synthesized speech instead of the original speech test recordings (which are protected by copyright), (b) when used at home, the sound is presented via the speaker in a reverberant environment, (c) the presented audio files are stored with lossy audio formats, and (d) the listener’s response is transcribed via ASR and not logged by an audiometrist. The application was implemented with the Alexa Skill Developer Kit in Python (Amazon Inc., 2018, version 1.10.2) and executed on an Amazon Echo loudspeaker (second generation). When the measurement application is started, the listener hears an instruction about the general measurement procedure and the structure of the hearing test. These instructions are based on the guidelines for the clinical application of the matrix sentence test (HörTech gGmbH, 2019). Since the subjects who participated in this study had no previous experience with smart speakers, they were told that the smart speaker is only listening when its optical indicator is active. During the measurement, the dialogue manager of the smart speaker uses the so-called intents. These define the intended actions a user wants to take with their spoken command and are defined by lexical patterns within our application that are matched to the ASR transcript. These intents trigger the next action when they are detected by the ASR component. The core intent of the measurement application is the response to a matrix stimulus sentence. The lexical patterns to invoke this intent are based on real responses obtained in previous work (Ooster et al., 2018), and the ASR engine of the smart speaker generalizes to variants of these responses. Based on this intent, the matrix test keywords in the subject’s response are collected, and the SNR for the next presentation is adapted based on the resulting score.

Figure 1.

Overview of the Smart Speaker Measurement Application.

We used a synthesized version of the sentences from the female German matrix sentence test, which was evaluated in a previous study (Nuesse et al., 2019). In that study, the same 150 sentences from the original female stimulus speaker (Wagener et al., 2014) were synthesized, and the commercial synthesis provided by the Acapela Group was found to produce the best results (in terms of naturalness as well as SRT results when compared with the original speaker). After checking the SNR of the smart speaker output by re-recording stimuli with known SNR and analyzing them, all synthesized sentences were premixed with the speech-shaped noise at steps of 0.1 dB and converted to the MP3 data format (MPEG version 2, 48 kbps, 16 kHz) as required for playback through the smart speaker. The noise signal was generated in the same way as for the original test by superimposing the synthesized speech material.

Evaluation Measurements

The goal of the evaluation measurements is to investigate two major factors that could influence the measurement reliability of speech audiometry conducted with a smart speaker: The first is the degree of hearing loss since the measurement application on the smart speaker should produce valid test results for all users. The second is the influence of different room acoustics that might considerably vary for at-home measurements.

Subject Groups

The listener groups differ by their degree of hearing loss, and the listeners’ age was also taken into account to explore age-related effects. In total, four subject groups were considered: Groups 1 to 3 covered age-matched elderly listeners categorized according to their Pure Tone Average (PTA) criterion from 0.5 to 4 kHz (Mathers et al., 2001): (a) normal hearing ( $\leq 25$ dB HL), (b) mildly HI ( $26 - 40$ dB HL), and (c) moderately HI ( $41 - 60$ dB HL). Group 4 consists of young NH listeners who satisfied a stricter definition of normal hearing, i.e., their hearing loss did not exceed 15 dB at any frequency with one possible exception: A hearing loss of 20 dB was allowed at one frequency from 250 Hz to 8 kHz. In total, 46 listeners participated in the study, with 9 to 16 subjects in each of the aforementioned groups (cf. Table 1).

Table 1.

Statistics of the Four Subject Groups Who Participated in the Evaluation.

	Young normal-hearing	Normal-hearing	Mild hearing loss	Moderate hearing loss
	Max. one frequency at 20 dB HL	PTA $< = 25$ dB HL	PTA $= 26 - 40$ dB HL	PTA $= 41 - 60$ dB HL
N (f/m)	16 (12/4)	9 (5/4)	11 (5/6)	10 (3/7)
Age	23 ± 4 years	61 ± 6 years	63 ± 6 years	62 ± 10 years
PTA	0 ± 5 dB HL	10 ± 9 dB HL	31 ± 5 dB HL	46 ± 6 dB HL

Note. PTA = Pure Tone Average.

All subjects were paid for participating in this study. The audiogram of the better hearing ear for each of the subjects is shown in Figure 2. The hearing loss was symmetrical ( $\leq 10$ dB HL difference in the PTA) for 43 listeners. Three listeners had an asymmetric hearing loss: One subject with a mild HL with 32.5 dB difference and two subjects with moderate HL with 12.5 dB and 15 dB difference, respectively. Three subjects in the elderly NH group showed hearing losses above 50 dB in the high frequencies, but still reached a PTA below 25 dB HL.

Figure 2

Individual Audiograms of the Better Hearing Ear of Our Subjects (Gray Lines) Together With the Average Audiogram for the Respective Subject Group (Black Lines). Note the different y-axis for the young normal-hearing listeners. The two dashed lines in the moderately hearing-impaired panel describe the audiogram from two subjects which had to be discarded from data analysis as explained in the Results section.

Test Conditions

The smart speaker measurements were conducted in a room referred to as Communication Acoustic Simulator (CAS) at the Hörzentrum Oldenburg. This room has a size of 12 m by 7 m by 2.8 m and uses 16 distributed microphones and 24 loudspeakers (12 flat wall-speakers and 12 two-way ceiling speakers) and a sound-regenerative Variable Room Acoustics System with a programmable microphone-to-speaker transfer function matrix to simulate different room acoustics. The subjects were seated at the center of the CAS with the smart speaker in front of them at a distance of 2 m. Before the actual measurement, the subjects were asked to adjust the volume of the speaker to a comfortable level with high intelligibility of the speech assistant’s voice; subjects were also allowed to change the volume of the speaker during the measurement.

To account for different acoustic conditions, rooms with different reverberation times T₃₀ were simulated: Living Room ( $T_{30} = 0.51$ s), Poor Classroom ( $T_{30} = 1.12$ s), and Concert Hall ( $T_{30} = 1.52$ s). With the assumption of a spherical sound source, this results in critical distances of 0.88 m, 0.59 m, and 0.51 m for the three simulated rooms, respectively. Hence, regardless of the simulated acoustic condition, the subjects were always in the far-field.

The clinical reference measurements were performed in a soundproof room at the Hörzentrum Oldenburg using speech signals from the original German female matrix sentence test speaker (Wagener et al., 2014). After D/A conversion (converter ADI-8 Pro by RME, Haimhausen, Germany), the speech and speech-shaped noise signals were amplified (HB7 by Tucker-Davis) and both presented to the subjects from the frontal direction via a loudspeaker (Mackie HR 824 by LOUD technologies). The distance between the subject and the loudspeaker was 1.4 m. The level of the speech-shaped noise was calibrated to 65 dB SPL in the absence of the listener using a measurement microphone (type 4189 by Brüel and Kjær) at the position of the listener and a sound level meter (Modular Precision Sound Analyzer; model 2260 by Brüel and Kjær). The speech test was performed using the Oldenburg Measurement Applications (HörTech; version 2.0.1.0). Responses of the speech intelligibility test were given orally by the listeners and marked by the (human) experimenter on a touch screen not visible to the listener.

Before conducting the main measurements, the audiogram was recorded with an audiometer (Aurical by Natus) with Sennheiser HDA200 headphones in a sound isolated booth using the ascending method.

Measurement Procedure

The subjects were invited for two measurement sessions each with nine SRT measurement lists with 20 presented matrix sentences, as described in Table 2. The first two measurement lists from each session were used as training due to the strong training effect of up to 2 dB in the first two measurement lists, which results from the limited vocabulary of the stimulus material of only 50 different words (Wagener et al., 1999). To make training more efficient, the first ten sentences of the first list of each session were presented without additional noise, so that each subject heard and understood each possible word of the matrix test at least once before the adaptive procedure started. After the respective training, each subject conducted two measurement lists in each of the three room settings, resulting in twelve measurement lists in total with the smart speaker application as well as two clinical reference measurement lists (one at the end of each of the two measurement sessions). Whenever the room acoustic settings were changed, the subjects heard four random sentences at different SNRs so they could adapt to the new room characteristics and could adjust the speaker volume if needed.

Table 2.

Measurement Sequence During One of the Two Sessions for Each Subject.

Room settings A	Training list 1
	Training list 2
	Test list 1
	Test list 2
Room settings B	Test list 3
	Test list 4
Room settings C	Test list 5
	Test list 6
Isolated booth	Reference

Note. While the reference measurement with the clinical setup was always performed at the end of each session, the order of the room characteristics of the CAS during the smart speaker measurement was randomly chosen for each subject, i.e., each setting (Living Room/Poor Classroom/Concert Hall) correspond to A, B, or C. CAS = Communication Acoustic Simulator.

The young NH subjects only conducted one measurement session with four measurement lists (plus two training lists and the clinical reference) with the Living Room settings, since the results from the elderly subjects showed only a minor influence of the room acoustic settings on the measurement results.

At the end of each measurement session, all recorded audio files in the cloud of the smart speaker were deleted to avoid speaker adaptation of the ASR system. During the ASR-based measurements, a human supervisor recorded the subjects’ responses to obtain the ground truth of responses (assuming that the experienced human supervisor produced no errors when logging the reported words). This human transcript was later used to determine ASR errors as reported in the next section.

Data Analysis

The data from the measurements are evaluated in three different ways: First—in order to evaluate the SRT measurement reliability of the smart speaker-based measurement system—the SRT results from the measurements with the smart speaker ( $S R T_{SmartSpeaker}$ ) are directly compared with the SRT_Reference measured with the clinical setup at the end of the respective measurement session. Second, the ASR transcription errors of the smart speaker system are analyzed, and third, by an analysis of the collected data we find criteria for passing/failing the test.

SRT Measurement Accuracy

The main measures of reliability in this study are the intrasubject standard deviation and the bias between $S R T_{SmartSpeaker}$ and SRT_Reference. The intrasubject standard deviation is obtained by calculating the standard deviation within each of the subjects and by averaging these standard deviations over the respective subject groups.

Performance of the ASR System

The ASR transcription errors are estimated by comparing the transcript from the ASR system with the labels generated in parallel to the measurement by the human supervisor. Errors are quantified by two measures, the Score Insertion Rate (SIR) and the Score Deletion Rate (SDR), which only take into account the errors that could actually have an influence on the SRT scoring (either by inserting or deleting a matrix word). Out-of-vocabulary words are ignored with this metric (as they are in the clinical measurement). The SDR and the SIR that quantify the performance of the ASR system are defined by

SIR = \frac{N_{score insertions}}{N}, SDR = \frac{N_{score deletions}}{N}

(1)

i.e., the number of errors

N_{score insertion}

and

N_{score deletion}

, which are normalized by the number of correctly repeated matrix sentence test words in the subject’s response N (ignoring nonmatrix words). This metric is evaluated on the list level, i.e., using responses to 20 stimuli with an average of 50 correctly repeated matrix sentence test words. Note that the order of the words is neglected in this error metric, since the order of the words is also ignored during scoring in the clinical tests. The full error rates in the classical sense of an ASR system were not calculated since the full transcript (including words that are not relevant for the score) was not created in parallel to the measurements.

SRT Decision Threshold for Providing User Feedback

To evaluate the performance of a decision threshold in terms of sensitivity and specificity, a potential value for a boundary is compared with three different reference decision criteria: (a) A deviation of the reference SRT measured with the clinical setup more than 1.96 standard deviations above the mean NH SRT, i.e., results outside the 95% percentile (which is the common approach for analyzing the result with the clinical setup), (b) a non-NH PTA (> 25 dB HL) based on the World Health Organization rules (Mathers et al., 2001), and (c) an audiogram-based indication for a hearing aid, which is in Germany given by a hearing loss of at least 30 dB in one of the audiogram frequencies between 500 Hz and 4 kHz (Gemeinsamer Bundesausschuss der Ärzte und Krankenkassen, 2012). In addition, the Youden-Index is derived which describes the ability of a decision threshold to separate the respective groups of data when sensitivity and specificity are equally weighted. To quantify how well the measure $S R T_{SmartSpeaker}$ is suited to determine one of the three reference criteria, we use the area under the curve (AUC) value, which describes the area under the receiver operating characteristic curve which is obtained by plotting the sensitivity over (1-specificity).

Results

SRT Measurement Accuracy

Figure 3 describes the SRT measurement accuracy with the smart speaker application compared with clinically acquired reference estimates. This figure shows the difference in the SRT outcome obtained with the smart speaker application to the reference measurement against the average of these two values for all of the subjects. The data from two moderately HI subjects have been removed from the analysis since their spread of SRT results was exceptionally high (intrasubject standard deviation of 3.46 dB) and they reached SRTs above 10 dB. For the first excluded subject, the ASR errors were very high ( $\approx 25 %$ on average); the second excluded listener spoke very softly during the first session, which resulted in several terminations of the measurement application. The second measurement sessions for the two subjects were performed both normally (presumably due to a certain familiarization to the speech interface of the speaker) but were also excluded from further analysis. While the zero-line in Figure 3 indicates a perfect match between the clinically measured value and the value estimated with the smart speaker application, most of the data points are above this line. This bias is highly significant (paired-sample t test, $p < 10^{- 3}$ ) and amounts to 1.40 dB on average (±2.63 dB 95% confidence interval, solid- and dashed-gray lines in Figure 3), i.e., the SRT measured with the clinical reference setup is lower than for the smart speaker condition. The different acoustic conditions are spread across a wide range of SRT differences, which is also reflected by the bias that is mostly constant over the three acoustic conditions (cf. Figure 4), and the only significant difference was the 0.38 dB between the poor classroom and the concert hall (paired-sample t test, Bonferroni-adjusted, $p_{living room / poor classroom} = 0.91, p_{living room / concert hall} = 0.15, p_{poor classroom / concert hall} = 0.02$ ). The data analyzed in this section are measured after presenting two training lists which compensates most of the training effect (Wagener et al., 1999). We did not observe any additional significant training effect over the course of the measurement sessions nor interactions with the room characteristics of the CAS with a two-way analysis of variance (Interaction: $F (10, 318) = 1.50$ , p = .14, training: $F (5, 318) = 0.81$ , p = .54). The differences between the subject groups are shown in Figure 5. The intrasubject standard deviation is increasing with increasing HL, reaching 1.09 dB for the elderly moderately HI listeners. On the other hand, the bias between the two measurement methods is decreasing for stronger HL from 2.21 dB for young NH listeners down to 0.67 dB for elderly moderately HI listeners. The bias from the young listeners is significantly higher than from the elderly subjects (two-sample t test, $p < 10^{- 6}$ ). Within the elderly listener group, the moderately HI listeners differ significantly from the other subgroups (two-sample t test, Bonferroni-adjusted, $p_{N H / mild} = 0.78, p_{N H / moderate} < 0.01, p_{mild / moderate} = 0.02$ ).

Figure 3.

Bland–Altman Plot for Visualizing the Agreement Between Automated and Regular Test Conduction. The figure compares SRT differences between smart speaker and clinical measurement (measured in the same session) to the average of these two values. Data are shown for different subject groups (labeled with different shapes) and room characteristics (differentiated by color). In addition, the average difference between the two measuring methods (gray solid line) and the 95% percentiles (gray dashed lines) are shown. The young-NH subjects only conducted experiments in the living room condition. SRT = speech recognition threshold; NH = normal-hearing; HL = hearing loss.

Figure 4.

Bias and Intrasubject Standard Deviations for All Elderly, Age-Matched Subjects for Different Room Configurations. The bias relates to the difference between clinical and automated measurements. A positive bias refers to a lower (better) SRT in the clinical measurement. SNR =signal-to-noise ratio.

Figure 5.

Bias and Intrasubject Standard Deviations in the Living Room-Settings for the Different Subject Groups. Since the young NH subject group only conducted one measurement session with one reference measurement, the intrasubject standard deviation for this subject group in the reference setup cannot be estimated. NH = normal-hearing; HL = hearing loss; SNR = signal-to-noise ratio.

Performance of the ASR System

The ASR performance of the smart speaker for all subjects is shown in Figure 6. The SIR is quite low with an overall average of 1.9% ( $\pm 1.0 %$ intersubject standard deviation) regardless of the subject group. We did not observe a significant difference between the elderly listener groups nor between the young and elderly listener groups (two-sample t test, Bonferroni-adjusted, $p_{N H / mild} = p_{N H / moderate} = p_{mild / moderate} = p_{young / elderly} = 1.0$ ). Among 424 single measurement lists, only two SIR outliers are observed in the group of elderly NH listeners (with an SIR above 11%). The SDR is higher than SIR (with an average of 6.1% [ $\pm 3.4 %$ intersubject standard deviation]), and larger SDR differences between the subjects were observed, which was positively correlated with the subjects’ age: The score deletion errors of elderly subjects (average $SDR = (6.7 \pm 5.6) %$ ) are significantly elevated compared with SDRs of young NH subjects ( $SDR = (4.2 \pm 3.8) %$ ) based on a two-sample t test and with the assumption of different variances ( $p < 10^{- 5}$ ). We did not find any significant differences between the different levels of hearing loss within elderly listeners (two-sample t test, Bonferroni-adjusted, $p_{N H / mild} = 1.0, p_{N H / moderate} = 0.683, p_{mild / moderate} = 1.00$ ).

Figure 6.

Violin Plot of the ASR System’s Performance From the Smart Speaker. The individual data points denote error rates of single measurement lists, each with 20 presented sentences; the width of the violin denotes the normalized histogram of the error rates. The median value and the interquartile range are denoted by white dots and the gray line, respectively. NH = normal-hearing; HL = hearing loss.

Furthermore, no significant difference was found in the SIR for different acoustic scenarios (two-sample t test, Bonferroni-adjusted, $p_{living room / poor classroom} = p_{living room / concert hall} = p_{poor classroom / concert hall} = 1.00$ ). The only significant room-related difference for SDR was observed between concert hall and poor classroom with a difference of $+ 2.1 %$ (two-sample t test, Bonferroni-adjusted, $p_{living room / poor classroom} = 0.08, p_{living room / concert hall} = 1.0, p_{poor classroom / concert hall} = 0.01$ ).

SRT Decision Thresholds for User Feedback

Figure 7 compares a potential decision threshold based on the $S R T_{SmartSpeaker}$ for the three reference criteria in terms of sensitivity, specificity, and the Youden-index. The 95% percentile decision threshold of –5.2 dB SNR, directly calculated from the young NH subjects data measured with the smart speaker, is marked as the dashed black line. The maximum of the Youden-index, i.e., the statistical optimal decision boundary (when sensitivity and specificity are weighted equally) is always below this threshold—(a) –6.0 dB, (b) –5.7 dB, and (c) –6.0 dB. At this threshold, the sensitivities for criteria (a) and (c) are higher than specificities—criterion (a) 0.93 versus 0.74 and criterion (c) 0.82 versus 0.70. For criterion (b), sensitivity and specificity have similar values at the threshold (0.75 vs. 0.73).

Figure 7.

Sensitivity and Specificity for Analyzing How Well a Potential $S R T_{SmartSpeaker}$ Decision Threshold Is Suited for Providing a Binary Screening Decision. The curves shown here are derived from criteria that are used to quantifying hearing loss. (a): $S R T_{Reference} > - 7.3$ dB SNR (the 95% percentile boundary from the young NH data measured with the reference setup).(b): PTA > 25 dB HL.(c): A hearing loss of 30 dB or higher in at least one audiogram frequency between 500 Hz and 4 kHz (which is an indication for a hearing aid in Germany). The dashed black line shows the 95% percentile boundary from the young NH data measured with the smart speaker. AUC = area under the curve; SRT = speech recognition threshold; SNR = signal-to-noise ratio.

Discussion

In this study, we investigated the SRT measurement reliability with a smart speaker-based application in three different acoustic conditions and with four different subject groups. First, we discuss the influence that errors made by the ASR system have on the measurement reliability. This is followed by the discussion of the overall SRT measurement reliability with the smart speaker application.

Effect of ASR Errors

In our previous study that investigated automated tests in a clinical setting (Ooster et al., 2018), we did not observe any significant decrease of the measurement reliability conditioned by the errors from the ASR system. Simulations regarding the overall influence of ASR errors on the SRT measurement accuracy in Ooster et al. (2018) matched well with the experimental findings. Even though these simulations are only based on NH SRT distributions, they are in principle valid for the smart speaker system explored in this study as well, since the tests are structurally identical and the same SNR adaptation scheme was used. On the one hand, the insertion errors are generally low (with an SIR of $(1.9 \pm 1.0) %$ ) and consequently it can be concluded that these errors barely influenced the measurement accuracy. On the other hand, the SDR is relatively high $(6.1 \pm 3.4) %$ which results in an elevated intrasubject standard deviation and an SRT bias (Figure 5), which is in line with the simulations from Ooster et al. (2018).

Even though extreme settings in the CAS were selected to evaluate the influence of different room acoustics, we did not observe strong differences in terms of ASR errors for different acoustic conditions (error rates for concert hall and poor classroom was the only significant difference in terms of SDR). We assume that the simulated acoustics resulted in a reduced diffuseness compared with real room acoustics, which could result in an underestimation of SRTs in that environment (i.e., SRT could appear as too good), although the simulated rooms appear to be very convincing.

For the best-performing group of young, NH listeners, average error rates up to 4% are obtained, which is quite high given the simple vocabulary. This can presumably be attributed to the missing fine tuning of the Alexa-based ASR back end and the overall challenging acoustic conditions with a distance of 2 m between subject and smart speaker. Compared with these younger subjects, the error rates for elderly listeners are significantly elevated (Figure 6). However, the degree of hearing-impairment does not affect the ASR component, i.e., there is no significant difference in the ASR errors between the different groups of hearing-impairment within the elderly listeners in our data. We speculate the experience in human–machine interaction to be the underlying reason for the contrast between young and old participants and for the generally elevated intrasubject standard deviations: For instance, longer pauses in responses triggered the end-point detection of the smart speaker, and the remainder of the response will be ignored, which in turn could result in an undesired SNR increase for the next test item. Experienced users of speech-based interfaces would probably adapt to the machine listener (as we already observed to some extent during the measurements of this study), and therefore our scoring results could represent a conservative estimate with respect to errors through user behavior. Long-term measurements with the same group of listeners should be conducted to test this hypothesis. Furthermore, we observed a few terminations of the measurement application when the next action was not triggered although the subject responded with words from the matrix sentence test, since the ASR system from the smart speaker did not recognize any of the words in the subject’s response correctly as matrix sentence words. This happened in about nine measurement lists from six different elderly listeners (one NH, four with mild HL, and one with moderate HL) among the overall 424 conducted measurement lists with the smart speaker. In that case, the subjects had to restart the measurement from the beginning.

Major companies that develop and sell smart speakers use distributed data processing, i.e., the ASR is performed in the cloud, in this case on servers from Amazon. These companies regularly update the ASR back end, over which developers and end users have no control. In theory, such an update could have a huge negative impact on the recognition of the matrix words, and the measurement accuracy described with the current setting could not be reached. However, changes to the back end are generally driven by the desire to increase the robustness and reliability of the system; it therefore seems unlikely to us that changes to the back end would reduce recognition performance; it appears to be more likely that future optimizations will improve ASR and therefore reduce the test bias and its standard deviation. Similarly, the smart speaker’s synthesized voice is used to describe the testing procedure to the listener, and developers also have no control over this component of the speech interface. For the actual test conduction described in this article, this does however not play a role since we used synthesized clean and noisy speech samples that were uploaded as part of the application, so this part remains unaltered even when the server-side synthesis is changed.

Bias and Reliability of SRT Measurements

The young NH subjects average SRT_Reference of ( $- 9.3 \pm 1.0$ ) dB using a female natural voice matches well the results from the literature with the female speaker, which was reported to be ( $- 9.4 \pm 1.0$ ) dB Wagener et al. (2014) and –9.1 dB Nuesse et al. (2019). When using synthetic speech in a clinical setup, a mean SRT of –8.5 dB was obtained for young NH subjects in a related study (Nuesse et al., 2019). This is on average 1.4 dB lower than the ( $- 7.1 \pm 1.0$ ) dB SRT for the young NH subjects measured with the smart speaker using synthetic speech, i.e., a bias exists between the two measurement methods (clinical vs. automated) even when using the same stimulus material. This bias is varying with the different levels of HL from 2.2 dB (young NH) down to 0.7 dB (elderly moderate HL) and therefore this main limitation to obtain accurate SRT results with the smart speaker. Nevertheless, the SRT results itself are reliable since the intrasubject standard deviation is independent from the simulated room acoustics and in the same range as with the clinical setup: The young NH intrasubject standard deviation with the smart speaker of 0.57 dB matches well with findings from other studies (0.5 dB in Brand & Kollmeier, (2002); Ooster et al., (2018)). Compared with this, the elderly NH listeners have a slightly elevated intrasubject standard deviation (0.69 dB) when using the smart speaker. The mild/moderate HI listeners’ intrasubject standard deviation of 0.93 dB/1.09 dB is slightly higher than the 0.9 dB found with 10 HI subjects (Wagener & Brand, 2005). The estimated intrasubject standard deviation values with the reference setup are smaller compared with the smart speaker measurement, but they only rely on two values from each listener before averaging over all respective listeners so these values might not be very reliable.

For the smart speaker measurements, the presentation level is not calibrated to a specific level, and the individual noise and speech levels could not be controlled for at all (as explained in the Methods section), which seems not to be crucial and is in line with previous studies: Wagener and Brand (2005) did not measure a significant influence of the presentation level on the SRT result (for levels that are clearly above the hearing threshold).

Note that all measurement reliability results are based on the measurement list after two training lists and there can be a training effect of up to 2 dB in the first two measurement lists (Wagener et al., 1999). This training effect can be a drawback for a screening procedure, as it increases the required measurement time to reach the optimal result. However, training will always reduce the SRT, i.e., an SRT result below the decision threshold is already valid after the first measurement. If the result exceeds the threshold, the system will therefore recommend to repeat the measurement up two times, so test users will learn the matrix test vocabulary while they are performing the screening.

Deriving User Recommendations from SRT Values

The 95% percentile criterion derived from the young NH data is used to analyze the recommendation outcome of the smart speaker-based measurement. The corresponding threshold is compared with different reference criteria based on the reference SRT measured with the clinical setup or the audiogram (which is only indirectly related to the measured SRT).

Overall, the smart speaker measurement shows a good classification performance with an AUC of 0.85 for predicting a PTA > 25 dB HL and an AUC value of 0.86 for predicting a hearing aid indication (cf. Figure 7(b) and (c)). This is slightly higher than the observed prediction performance of the telephone-based German digit triplet test for which an AUC value of 0.82 was observed on 1903 listeners for predicting a PTA > 25 dB HL and an AUC value of 0.76 for predicting a hearing aid indication (von Gablenz et al., 2014). For the computer-based headphone conduction of the English digit triplet test, an AUC value of 0.95 was found for 20 NH and 20 HI listeners (Folmer et al., 2017) for predicting a PTA > 25 dB HL, whereas De Sousa et al. (2020) could increase the AUC from 0.78 with diotic stimuli in a smartphone-based headphone presentation to 0.94 with antiphasic stimuli on 145 listeners.

The prediction performance reported in this article can also be compared with other speech audiometric tests: Smits et al. (2004) found an AUC of 0.97 with the digit triplet test in comparison to the Plomp sentence test (Plomp & Mimpen, 1979) with 38 subjects tested with headphones, and von Gablenz et al. (2014) measured an AUC of 0.70 when comparing the digit triplet test over telephone to the Göttinger sentence test (Kollmeier & Wesselkamp, 1997) in a clinical setup. Therefore, the overall classification performance of the smart speaker-based measurement can be rated high with an AUC of 0.95 (cf. Figure 7(a)) for detecting a clinical elevated SRT.

Limitations of This Study

An important limitation of this study is that experiments were conducted in a laboratory environment with simulated room acoustics. Even though the small influence of the different simulated acoustics seems promising, future evaluations should take into account environments such as the private homes of the listeners with their respective real room acoustics for capturing the full variability of real-life scenarios. Furthermore, even though all the instructions for the test were given by the smart speaker application, a human supervisor was always present during the measurement which could have influenced the behavior of the listeners. The listeners who participated in this study were naive users of such a smart speaker. Experienced users potentially can profit from two training effects, i.e., adaptation of the speaker to its main user (which would decrease ASR errors) and secondly a training of the user, since long-term users of speech assistance presumably learn how to best interact with a virtual assistant (thereby avoiding incorrect end-point detection). Finally, the threshold defined in this article was obtained from measurements performed after training. For untrained listeners, the number of false positives should be higher compared with the presented values, and at the same time the number of false negatives should be lower. It is therefore important to note that the decision boundaries derived from our experiments are valid for trained listeners only. In the smart speaker application, this is considered by recommending a repetition of the test (which effectively trains the users).

Conclusions

This article introduced smart speaker-based speech audiometry with the matrix sentence test. We presented results for young and older listeners with different degrees of hearing loss in three different simulated room acoustic conditions. For the different simulated room acoustics, we observe only small differences in the measured SRTs and the performance of the smart speaker’s ASR system. The different listener groups showed a slightly decreased measurement reliability in terms of intrasubject standard deviation in comparison to results with the clinical version in the literature. The ASR performance is significantly worse for elderly listeners, which appears to be the main source of the reduced reliability. However, the main limitation for obtaining accurate SRT results is a varying bias between the listener groups, which ranges from + 0.7 dB for elderly moderately HI listeners up to + 2.2 dB for young NH listeners. Nevertheless, the data presented in this article support the conclusion that smart speaker-based speech audiometry can reliably detect a deviating SRT in a self-guided manner at home since the receiver operating characteristic analysis showed an AUC of 0.95 for detecting a deviating clinical SRT, where the 95% percentile threshold based on the young NH data results in 93% sensitivity and 74% specificity. The smart speaker-based speech audiometric testing therefore seems promising for complementing clinical tests with the advantage of at-home screenings.

Footnotes

Acknowledgments

The authors would like to thank Inga Holube and Theresa Nüsse for providing the synthesized matrix test stimulus sentences as well as Timmy Kröger and Dirk Eike Hoffner for their help with the conduction of the evaluation measurements. Data from a pilot study (covering responses from 15 listeners compared with 46 listeners presented here) have been presented at the ISAAR symposium 2019 in Nyborg (Ooster et al., 2020).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy—EXC 2177/1 (Hearing4all)—Project ID 390895286 and the CRC TRR 31, Transfer Project T01.

ORCID iDs

Jasper Ooster

Kirsten C. Wagener

Bernd T. Meyer

Note

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Speech Audiometry at Home: Automated Listening Tests via Smart Speakers With Normal-Hearing and Hearing-Impaired Listeners

Abstract

Keywords

Methods

Matrix Sentence Test

The Smart Speaker Application

Evaluation Measurements

Subject Groups

Test Conditions

Measurement Procedure

Data Analysis

SRT Measurement Accuracy

Performance of the ASR System

SRT Decision Threshold for Providing User Feedback

Results

SRT Measurement Accuracy

Performance of the ASR System

SRT Decision Thresholds for User Feedback

Discussion

Effect of ASR Errors

Bias and Reliability of SRT Measurements

Deriving User Recommendations from SRT Values

Limitations of This Study

Conclusions

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iDs

Note

References