Recently, pupillometry has been combined with fixational eye movements known as microsaccades to quantify the arousal and attentional components of listening effort. However, it is unclear what effect hearing status has on these combined measures. The goal of this study was to evaluate dynamic patterns of sustained arousal and attentional allocation using pupillometry and microsaccade measures, respectively. Twenty-two adults with hearing impairment modeled as a continuous function of pure-tone average, and 28 adults without hearing impairment listened to sentences and affective sounds while their pupil size and microsaccade activity were recorded. Participants with hearing impairment exhibited different time-course pupil responses and altered microsaccade rates than those with normal hearing, suggesting differences in arousal and attentional engagement. These findings represent new dynamic relationships between arousal and attention in participants with and without hearing loss and suggest a utility for measuring these dissociable responses concurrently with pupillometry and microsaccades.
A substantial proportion of the population has clinically normal hearing thresholds yet experiences difficulty understanding speech in background noise. Existing hearing models explain these difficulties using a limited number of physiological and signal-processing mechanisms, implying that relationships among psychoacoustic measures may reflect shared underlying processes. However, these relationships have rarely been examined comprehensively within the same participants. Here we quantified associations among psychoacoustic measures using linear regression and principal component analysis. Speech-in-noise, tone-in-noise in different noise types, and loudness growth functions were obtained and analyzed in monaural and binaural listening conditions in 52 volunteers (aged 20-80 years) with age-typical or mild hearing loss (PTA4 < 32.5 dB HL). Linear regression analyses identified direct psychoacoustic measures - particularly interaurally antiphasic tone-in-noise (N0Sπ) thresholds - as robust indicators of suprathreshold hearing function. Although N0Sπ showed only a moderate association with the corresponding diotic thresholds (N0S0), it was more strongly associated with poorer-ear speech-in-noise thresholds (r = 0.60) than the corresponding 500-Hz audiometric threshold (r = 0.35), suggesting that this relationship was not driven by audibility alone. Exploratory principal component analysis further showed that N0Sπ thresholds contributed to multiple auditory dimensions associated with speech-in-noise variability. In contrast, derived measures such as binaural masking level difference or auditory filter bandwidth showed weaker and less consistent associations. Overall, the results indicate that selected direct psychoacoustic measures may provide a more robust characterization of suprathreshold auditory function than derived measures.
Objective speech intelligibility prediction methods provide a low-cost alternative to tests with human listeners, allowing the investigation of many factors affecting intelligibility. However, the objective prediction of speech intelligibility for Mandarin-speaking listeners with hearing impairment is largely underdeveloped. Existing intelligibility metrics are based on English linguistic structures and/or are designed primarily for normal-hearing listeners. This paper introduces a Mandarin Chinese Hearing-Aid Speech Perception Index (MCHASPI), an objective metric for predicting the intelligibility of speech processed by hearing aids for Mandarin Chinese speakers with or without hearing loss. A key challenge was the scarcity of high-quality datasets and the lack of models tailored to the perceptual characteristics of Mandarin Chinese. To address this, we first constructed a Mandarin intelligibility dataset using subjective hearing tests, for speech with six types of distortions. A hearing loss simulator was employed to preprocess the reference (undistorted) and test speech signals. Multiple features were extracted from these processed signals and combined for subsequent analysis. Two prediction models were developed, namely MCHASPI-NN and MCHASPI-L, utilizing a neural network and a logistic mapping function, respectively, to predict intelligibility from feature values. The core of the MCHASPI model involves a perceptually motivated segmental stretching scheme for precise signal alignment, followed by frame-wise threshold detection and multi-feature fusion. Evaluations with normal-hearing and hearing-impaired listeners demonstrated that MCHASPI gave significantly more accurate predictions than existing models across all tested datasets. This validates the effectiveness of MCHASPI for assessing Mandarin speech intelligibility for populations with varying degrees of hearing loss.
This study evaluated how interaural-time-difference (ITD) thresholds for bilateral cochlear-implant listeners were related to monaural and binaural factors associated with behavioral loudness perception and objective measures associated with the interface between electrode contacts and target neurons (the electrode-to-neural interface, ENI). For 11 bilateral cochlear-implant listeners, ITD thresholds for number-matched interaural electrode pairs were measured at five places along the array. On these same electrodes, the most comfortable current level used for stimulation (C-level), electrically evoked compound action potentials (ECAPs), and electrode positions relative to the modiolus based on computed-tomography scans were recorded. Monaural predictors included stimulation level (quantified by C-levels), modiolar distance (related to the width of neural activation), and efficacy of auditory nerve fiber responses to electrical stimulation [ECAP amplitude growth function (AGF) slopes]. Binaural predictors included the degree of interaural asymmetry in both C-levels and modiolar distances. It was hypothesized that better ITD sensitivity would be related to higher C-levels and larger modiolar distances (because of increased loudness and broader neural activation), steeper ECAP AGF slopes (because of more numerous and healthy neurons), and smaller interaural asymmetry (because of a larger proportion of binaurally excited neurons). The hypotheses were tested via backwards elimination in mixed-effects models with bias and stability analyses performed using bootstrapping. Partially confirming the hypotheses, higher C-levels and greater modiolar distance for electrodes in the ear with better ENI (as defined primarily by the ECAP AGF slope) were significantly associated with better ITD thresholds. These results highlight the role of monaural processing in binaural performance.
A conversational speech-in-noise test administered without human supervision is desirable to reduce the need to attend the clinic. This study aimed to create and evaluate a novel question-and-answer-based speech-in-noise test called ‘CHAT’, where stimuli were synthesised using text-to-speech (TTS), responses captured using automatic speech recognition (ASR), and responses evaluated using a large language model (LLM). Three tests were compared: the newly developed CHAT, the Adaptive Sentence List (ASL) and the Four Alternative Auditory Feature Test (FAAF). CHAT used synthetic speech while ASL and FAAF by default used human-recorded speech. Speech reception thresholds (SRT) of 20 participants with normal hearing and 20 with mild to moderate hearing loss were measured using these three tests. Validity was evaluated using receiver operating characteristic (ROC) analysis and correlations between SRTs and average pure-tone audiometry (PTA), across the three speech tests. Reliability was evaluated by comparing limits-of-agreement between SRTs achieved by repeated runs of CHAT and ASL tests using Bland-Altman analysis. Results revealed limits-of-agreement between repeated runs of CHAT and ASL were ±3.6 dB and ±3.1 dB, respectively. The area under the ROC curve was 0.97 for CHAT, 0.95 for ASL and 0.89 for FAAF. The correlation between SRT and PTA results was 0.86 for CHAT, 0.82 for ASL and 0.79 for FAAF. These findings indicate that CHAT demonstrates reliability and validity comparable to human-recorded ASL and FAAF, supporting the use of synthetic speech for speech-in-noise testing. Overall, the results suggest that AI tools can help create and administer conversational speech-in-noise tests.
