Vijay Parsa

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: Continuing education in hearing health care encompasses a range of opportunities, including formal academic training, professional certification and credentialing, technical or job-specific instruction, professional development events, and personal or general skill enhancement. This study examined the continuing education experiences and needs of hearing health care professionals to inform the design and delivery of future educational initiatives tailored to this workforce. Method: Members of the National Centre for Audiology at the University of Western Ontario developed a three-part, 40-item online survey to gather information from hearing health care professionals regarding demographic characteristics, experiences and needs related to delivery method, financial considerations, education requirements, professional interests, and learning style alignment. Results: Eighty-one professionals aged between 18 and 64 years participated in this survey-based study. Most respondents were from North America and reported previous participation in some form of continuing education opportunity within the past year, with cost reported as a limiting factor to participation. Various learning opportunities, including developing new hands-on skills, were reported to benefit the respondents' learning styles. Future opportunities of interest were noted to include extension courses and professional certifications. Conclusions: This study highlights the importance of continuing education opportunities specific to the professional development of hearing health care professionals. The understanding of past and future continuing education trends informed the need for targeted, accessible opportunities with a focus on active learning opportunities and those available online in real time. Supplemental Material: https://doi.org/10.23641/asha.32288262

Hypernasality is a common characteristic of several speech disorders and can significantly affect perceived speech intelligibility and quality. Nasometry quantifies nasalance by calculating the proportion of acoustic energy emitted from the nasal cavity relative to the combined nasal and oral acoustic output during speech production and is commonly used in clinical assessment and research. However, commercially available nasometers are costly and limited in portability, restricting their use in resource-limited or remote settings. The primary purpose of this study was to design and build a low-cost, open-source mobile nasometer prototype (“mNasometer”) by leveraging advances in 3D printing, off-the-shelf electronic components, and a custom open-source mobile application. A secondary aim was to compare the electroacoustic and subjective performance of mNasometer with that of a gold-standard commercial nasometer. Electroacoustic analyses focused on comparing long-term averaged spectra and the oral/nasal acoustic isolation between the gold-standard commercial nasometer and the proposed mNasometer, which incorporates a 3D-printed nasal separation plate. In addition, nasalance scores were collected from ten healthy young adult participants using both systems during structured speech production tasks (i.e., reading standard passages or nasal sentences). Agreement between devices was evaluated using correlational analyses and comparative statistical procedures. Long-term averaged spectra exhibited similar profiles between the commercial nasometer and the mNasometer across different test stimuli, indicating comparable capture of stimulus energy distributions. Although the mNasometer demonstrated reduced oral–nasal acoustic isolation relative to the commercial system, objective nasalance scores followed similar overall trends between devices, with statistically significant stimulus-dependent differences observed. Frame-wise correlational analyses revealed significant correlations between nasalance measures obtained from the commercial nasometer and the mNasometer across most of the speech production tasks, suggesting that the reduced isolation did not critically compromise measurement correspondence. In summary, the low-cost, open-source mNasometer prototype provides nasalance measurements that show promising agreement with those of a gold-standard commercial device. Its reduced cost and increased portability suggest potential for expanded research and field-based applications in the objective assessment of nasalance.

In this study, we propose a novel automated model for speech quality estimation that objectively evaluates perceptual dysphonia severity and breathiness in audio samples, demonstrating strong correlation with expert ratings. The proposed model integrates Whisper encoder embeddings with Mel spectrograms augmented by second-order delta features combined with a sequential-attention fusion network feature mapping path. This hybrid approach enhances the model’s sensitivity to phonetic, high-level feature representation, and spectral variations, enabling more accurate predictions of perceptual speech quality. A sequential-attention fusion network feature mapping module captures long-range dependencies through the multi-head attention network, while LSTM layers refine the learned representations by modeling temporal dynamics. Comparative analysis against state-of-the-art methods for dysphonia assessment demonstrates our model’s better correlation with clinician’s judgments across test samples. Our findings underscore the effectiveness of ASR-derived embeddings alongside the deep feature mapping structure in disordered speech quality assessment, offering a promising pathway for advancing automated evaluation systems.

In this study, we proposed a novel automated speech quality estimation model capable of evaluating perceptual dysphonia severity and breathiness in audio samples, ensuring alignment with expert-rated assessments. The proposed model integrates Whisper ASR embeddings with Mel spectrograms augmented by second-order delta features combined with a sequential-attention fusion network feature mapping path. This hybrid approach enhances the model’s sensitivity to phonetic, high level feature representation and spectral variations, enabling more accurate predictions of perceptual speech quality. A sequential-attention fusion network feature mapping module captures long-range de-pendencies through the multi-head attention network, while LSTM layers refine the learned representations by modeling temporal dynamics. Comparative analysis against state-of-the-art methods for dysphonia assessment demonstrates our model’s superior generalization across test samples. Our findings underscore the effectiveness of ASR-derived embeddings alongside the deep feature mapping structure in speech quality assessment, offering a promising pathway for advancing automated evaluation systems.

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: 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.

Medical image processing applications typically demand highly accurate image segmentation. However, existing segmentation approaches exhibit performance degradation when faced with diverse medical imaging modalities and varied segmentation target sizes. In this paper, we propose and evaluate a dilated One-to-Many U-Net deep learning model that addresses these challenges. The proposed model comprises of four rows of encoder-decoder modules, with each module consisting of three trainable blocks with different layers. The last three rows of the U-Net are extended versions of the three blocks in the first row, with the encoder-decoder blocks connected through the skip connections to the previous rows. The outputs of the last blocks from the last three rows in the decoder are concatenated, and finally, a dilation network is employed to improve the small target segmentation in different medical images. Two datasets have been used for the evaluation: the HC18 grand challenge ultrasound dataset for fetal head segmentation and the Multi-site MRI dataset, including the BIDMC and HK sites, for prostate segmentation in MRI images. The proposed approach achieved Dice and Jaccard coefficients of 96.54% and 93.93%, respectively, for the HC18 grand challenge dataset, 96.76% and 93.97% for the BIDMC site dataset, and 92.58% and 86.96% for the HK site dataset. Statistical analyses showed that the proposed model outperformed several other U-Net-based models.

Many hearing difficulties can be explained as a loss of audibility, a problem easily detected and treated using standard audiological procedures. Yet, hearing can be much poorer (or more impaired) than audibility predicts because of deficits in the suprathreshold mechanisms that encode the rapidly changing, spectral, temporal, and binaural aspects of the sound. The ability to evaluate these mechanisms requires well-defined stimuli and strict adherence to rigorous psychometric principles. This project reports on the comparison between a laboratory-based and a mobile system’s results for psychoacoustic assessment in adult listeners with normal hearing. A description of both systems employed is provided. Psychoacoustic tests include frequency discrimination, amplitude modulation detection, binaural encoding, and temporal gap detection. Results reported by the mobile system were not significantly different from those collected with the laboratory-based system for most of the tests and were consistent with those reported in the literature. The mobile system has the potential to be a feasible option for the assessment of suprathreshold auditory encoding abilities.

This paper investigated the performance of a number of acoustic measures, both individually and in combination, in predicting the perceived quality of sustained vowels produced by people impaired with Parkinson’s disease (PD). Sustained vowel recordings were collected from 51 PD patients before and after the administration of the Levodopa medication. Subjective ratings of the overall vowel quality were garnered using a visual analog scale. These ratings served to benchmark the effectiveness of the acoustic measures. Acoustic predictors of the perceived vowel quality included the harmonics-to-noise ratio (HNR), smoothed cepstral peak prominence (CPP), recurrence period density entropy (RPDE), Gammatone frequency cepstral coefficients (GFCCs), linear prediction (LP) coefficients and their variants, and modulation spectrogram features. Linear regression (LR) and support vector regression (SVR) models were employed to assimilate multiple features. Different feature dimensionality reduction methods were investigated to avoid model overfitting and enhance the prediction capabilities for the test dataset. Results showed that the RPDE measure performed the best among all individual features, while a regression model incorporating a subset of features produced the best overall correlation of 0.80 between the predicted and actual vowel quality ratings. This model may therefore serve as a surrogate for auditory-perceptual assessment of Parkinsonian vowel quality. Furthermore, the model may offer the clinician a tool to predict who may benefit from Levodopa medication in terms of enhanced voice quality.