Paula Folkeard

Purpose: The purpose of this study was to evaluate remote microphone performance across three hearing aid brands using various simulated electroacoustic verification methods to optimize speech perception outcomes in a real-world environment. Method: This study was conducted using manikin recordings at varying azimuth angles between a simulated talker and listeners at a real-world gymnasium and employing a predictive speech perception metric (Hearing Aid Speech Perception Index). The listener manikins were programmed to simulate mild, sloping hearing loss and fitted with three brands of premium receiver-in-canal hearing aids, including their respective proprietary remote microphones (both low and high technology levels). Remote microphones were verified electroacoustically in a test box for transparency and maximum audible output frequency (MAOF), alongside signal-processing features. Results: Remote microphone verification using MAOF was a strong predictor of remote microphone performance in the real-world gymnasium. Furthermore, remote microphones consistently produced higher speech perception scores relative to hearing aids alone across all brands, regardless of the factor of azimuth. Conclusions: Remote microphone verification using MAOF, alongside the recommended transparency approach, can support optimal real-world performance. Future behavioral assessments of remote microphones in real-world environments with individuals with hearing loss are discussed. Supplemental Material: https://doi.org/10.23641/asha.32065932

Purpose: The use of hearing aid–based step counting may offer objective measurement of activity, but there is little published literature on the perspectives of older adult users in using this feature. The purpose of this study was to qualitatively describe the experiences and motivation for using a hearing aid step-counting feature in a sample of active older adults. Method: A prospective qualitative research design was employed with the use of focus groups with active older adult participants who were fitted with hearing aids with a step-counting feature. Twelve older adults with hearing loss (age range: 64–88 years) were recruited in this study. Half of the participants were experienced hearing aid users. Participants were recruited from two physical activity groups with varying intensity levels. Results: The majority of participants were physically active and had prior experience using step-counting features on alternative devices. Major themes were Goal Setting and Health Monitoring, Device Use, and Movement and Motivation Factors. Background experiences and individual perspectives related to the reasons and motivations for using hearing aids for monitoring daily step count were the major subthemes. Conclusions: Participants who engaged in light-intensity physical activity were more likely to be motivated to monitor daily step count using the hearing aids. Participants' perspectives on the reason for motivation varied between cursory monitoring to stay consistently active and striving to meet a daily step objective. Supplemental Material: https://doi.org/10.23641/asha.32159181

Deep neural network (DNN)-based noise reduction has emerged as a promising advancement in hearing aid signal processing as a means to improve speech intelligibility in noisy environments for hearing aid wearers. The aim of this study was to investigate the impact of the DNN on speech intelligibility in speech-shaped noise (SSN) and multitalker babble (MTB) when the target speech was coming from the front and from the side. Subjective ratings of clarity, total impression, listening effort, and background noise awareness were also collected. Twenty adult participants with mild to moderately severe sensorineural hearing loss were fitted with hearing aids from a single manufacturer, programmed with four different settings that varied across combinations of microphone directionality (omnidirectional and directional beamforming) and noise reduction (off, traditional, DNN). Results showed that DNN, when combined with beamforming, consistently outperformed the other programs across all metrics. Outcomes were influenced by both noise type and spatial configuration. DNN was more effective in SSN than MTB. Beamforming was especially beneficial when the target speech came from the front. Listening in programs that included both DNN and beamforming together resulted in additional benefits shown in the outcome measures, most likely due to the beamforming improving the signal-to-noise ratio and providing a cleaner signal for the DNN to work with.

Purpose: Individuals with hearing loss typically experience greater listening effort, which is the additional recruitment of cognitive/mental resources such as attention and memory to understand speech and can be aversive and tiring. Reducing effort is an important goal of the hearing health care industry. Pupillometry is an objective and increasingly popular measure of listening effort, but gold standard measures of pupil size are expensive and unwieldy. The purpose of this study was to compare a low-cost and portable pupillometry device (Gazepoint GP3 HD) to a more traditional gold standard pupillometry tool (EyeLink 1000) for indexing listening effort via pupil size. Method: Twenty normal-hearing young adults (age range: 18–23 years) were recruited in this study. Participants' pupil size was measured using the Gazepoint and EyeLink pupillometry devices while listening to Hearing in Noise Test sentences in stationary speech–shaped noise at signal-to-noise ratios (SNRs) ranging from −8 to +8 dB. Results: Participants' word report accuracy increased from approximately 12% to 100% when the SNRs increased from −8 to +8 dB. Peak pupil diameter decreased for both devices and was smaller with the Gazepoint device. Data quality was comparable for the two devices. Conclusion: Gazepoint appeared to be an effective pupillometry device that records pupil dilation across a wide range of SNRs, without interfering with the auditory task.

Purpose: The Connected Speech Test (CST) assesses an individual's ability to understand everyday contextualized running speech amidst competing background babble. To minimize accent effects on speech perception scores and reduce the noise floor of the original recordings, an updated version was developed by Saleh et al. (2020). The updated recordings feature a speaker with a General American accent to replace the Southern U.S. accent in the original test, and modern recording equipment was used to achieve a lower noise floor. The aim of this study was to collect normative data, characterizing performance on the updated CST. Self-reported speech intelligibility and listening effort ratings were collected to examine how subjective perceptions of the task vary across test conditions. Method: To evaluate normative performance on this updated test, 40 native English-speaking adults (36 females and four males) with normal hearing were recruited from The University of Western Ontario. Multitalker babble was presented at a fixed level, and speech was presented at fixed signal-to-babble ratios (SBRs) to participants in both co-located and separated loudspeaker conditions. At each SBR, participants were scored based on key words correctly identified. Subjective speech intelligibility and listening effort were evaluated using self-report scales. For each measure, data were fitted with transfer functions to characterize performance on the task. Results: Participants demonstrated significantly better performance in the separated loudspeaker condition, indicating a spatial release from masking. For both conditions, increased SBR was associated with increased performance and subjective speech intelligibility, and decreased listening effort. Conclusion: The study provides normative data to characterize expected performance for the updated version of the CST.

This study evaluated a deep-neural-network denoising system using model-based design, comparing it with adaptive filtering and beamforming across various noise types, SNRs, and hearing-aid fittings. A KEMAR manikin fitted with five audiograms was recorded in reverberant and non-reverberant rooms, yielding 1,152 recordings. Speech intelligibility was estimated using the HASPI from 1,152 KEMAR manikin recordings. Effects of processing strategy and acoustic factors were tested with model-based within-device design that account for repeated recordings per device/program and fitting. Linear mixed model results showed that the DNN with beamforming outperformed conventional processing, with strongest gains at 0 and +5 dB SNR, moderate benefits at −5 dB in low reverberation, and none in medium reverberation. Across SNRs and noise types, the DNN combined with beamforming yielded the highest predicted intelligibility, with benefits attenuated under moderate reverberation. Azimuth effects varied; because estimates were derived from a better-ear metric on manikin recordings. Additionally, this paper reports comparisons using metrics of sound quality, for an intrusive metric (HASQI) and the pMOS non-intrusive metric. Results indicated that model type interacted with processing and acoustic factors. HASQI and pMOS scores increased with SNR and were moderately correlated (r² ≈ 0.479), supporting the use of non-intrusive metrics for large-scale assessment. However, pMOS showed greater variability across hearing aid programs and environments, suggesting non-intrusive models capture processing effects differently than intrusive metrics. These findings highlight the promise and limits of non-intrusive evaluation while emphasizing the benefit of combining deep learning with beamforming to improve intelligibility and quality.

Purpose: Best practices in audiology recommend assessing speech understanding in noisy environments, especially for those with communication difficulties. Speech-in-noise (SiN) assessments such as the QuickSIN are used for validating signal processing in hearing aids (HAs) and are linked to HA satisfaction. This project seeks to enhance QuickSIN test efficiency by applying recent advancements in automatic speech recognition (ASR) technologies. Method: Twenty-three adults with sensorineural hearing loss were fitted bilaterally with Unitron Moxi HAs and were administered the QuickSIN test in low and high reverberation environments. Testing was performed with two different HA programs: an omnidirectional program and a fixed directional microphone program. QuickSIN sentences were presented from 0° azimuth and competing babble from either 0°, laterally from 90° or 270°, or simultaneously from 90°, 180°, and 270° azimuths. Participants’ verbal responses to QuickSIN stimuli were scored by an audiologist and were recorded in parallel for offline transcription and scoring by ASR models from Amazon, Microsoft, NVIDIA, and Picovoice. The ASR-derived QuickSIN scores were compared to the corresponding audiologist-derived scores. Results: Repeated Measures ANOVA results revealed that all ASR models overestimated the QuickSIN scores across most test conditions. Bland-Altman analyses showed that the Amazon ASR model had the least bias and the narrowest range for the limits of agreement, in comparison to the manual scoring by an experienced audiologist. Conclusions: Some ASR models, such as Amazon, demonstrated performance comparable to that of an audiologist in automatically scoring QuickSIN tests. However, further refinements are necessary to increase the robustness of the ASR models in scoring low SNR loss test conditions.

The purpose of the study was to qualitatively describe the experiences of hearing aid and physical fit accessories use during physical activity and exercise participation in a sample of older adults with hearing loss. A prospective qualitative research design was employed with the use of focus groups with older adult participants who were fitted with hearing aids and physical fit accessories. Twelve older adults with hearing loss (six experienced and six new hearing aid users, age range 64 - 88 years) were recruited in this study. The barriers to hearing at physical activity and exercise environments were related to reverberation, loud music, and instructor's location and position relative to exercise members, whereas facilitators were aided hearing, the instructor projecting their voice and demonstrating the moves. The most preferred physical fit accessories were the retention lock and the corded and cordless hearing aid sleeves. The least preferred accessories were the hearing aid-to-glasses connector and stick'n stay tape. Individual differences and needs were factored into different perspectives on hearing aid and physical fit accessory use, emphasising patient-centered approach when coaching and counselling on device use for physical activity and exercise.

Purpose: This study aimed to evaluate conversation hearing with an adaptive beamforming hearing aid that supports adaptive tracking of multiple talkers in an ecologically valid, real-world food court environment in a busy mall. Method: Twenty older adult experienced hearing aid wearers with sensorineural hearing loss were fitted in the lab with binaural receiver-in-the-canal style hearing aids set with two programs, each having a different beamforming strategy. The participant and two researchers then met in a moderately noisy and reverberant food court at a local mall where the participant was asked to listen to a conversation between the two researchers. Participants rated the extent of their agreement with 10 positively worded statements specific to the conversation twice, once for each program. Participants then provided program-preference ratings for seven different aspects of a conversation during which the programs were switched back and forth by the researcher, so that participants were unaware of the condition to which they were listening. Results: Real-world subjective ratings for all domains resulted in positive values on average for both programs. Pairwise comparisons indicated that the intervention algorithm had higher absolute ratings on five of the 10 criteria including understanding, clarity, focus, listening effort, and background noise. Ratings for preference between programs indicated a significant preference for the intervention algorithm for all seven criteria. Conclusions: In a real-world setting, the use of hearing aids with separate processing of sounds from the front and back hemisphere provided positive subjective ratings. However, following a group conversation with multiple conversation partners, improvements in the algorithm to account for the locations of interlocutors and the natural head turning of the hearing aid wearer that occurs during a conversation by adding and controlling multiple adaptive beams in the front hemisphere significantly influenced preference for all aspects rated.

To develop and validate a novel questionnaire aimed at providing a structured, evidence-based methodology for hearing aid recommendation and selection using self-reported importance ratings for different modern hearing aid features. The initial questionnaire items were created through a concept mapping approach that involved input from hearing aid users. Hearing care professional focus groups (n = 10) were conducted to assess questionnaire content and design, and to guide modifications. Validation of this initial 34-item version of the questionnaire was conducted using an anonymous online survey tool (Qualtrics). Exploratory factor analysis was used to assess the factor structure of the dataset, using principal axis factoring. Questionnaire reliability and inter-item correlation were assessed. Items with low factor loading and high cross-loading were removed. Two hundred and eighteen adult participants with a self-reported hearing loss (median age = 48 years, range = 18-95 years) completed the questionnaire. Analysis and item removal resulted in a 28-item questionnaire. Three factors were identified, dividing the hearing aid features into the subscales: "Advanced connectivity & streaming", "Physical attributes & usability", and "Sound quality & intelligibility". This study has resulted in a patient-oriented questionnaire that allows clinicians to gather patient input in a structured manner.