Trends in Hearing最新文献

To date, pure-tone audiometry remains the gold standard for clinical auditory testing. However, pure-tone audiometry is time-consuming and only provides a discrete estimate of hearing acuity. Here, we aim to address these two main drawbacks by developing a machine learning (ML)-based approach for fully automated bone-conduction (BC) audiometry tests with forehead vibrator placement. Study 1 examines the occlusion effects when the headphones are positioned on both ears during BC forehead testing. Study 2 describes the ML-based approach for BC audiometry, with automated contralateral masking rules, compensation for occlusion effects and forehead-mastoid corrections. Next, the performance of ML-audiometry is examined in comparison to manual and conventional BC audiometry with mastoid placement. Finally, Study 3 examines the test-retest reliability of ML-audiometry. Our results show no significant performance difference between automated ML-audiometry and manual conventional audiometry. High test-retest reliability is achieved with the automated ML-audiometry. Together, our findings demonstrate the performance and reliability of the automated ML-based BC audiometry for both normal-hearing and hearing-impaired adult listeners with mild to severe hearing losses.

Listeners with normal audiometric thresholds show substantial variability in their ability to understand speech in noise (SiN). These individual differences have been reported to be associated with a range of auditory and cognitive abilities. The present study addresses the association between SiN processing and the individual susceptibility of short-term memory to auditory distraction (i.e., the irrelevant sound effect [ISE]). In a sample of 67 young adult participants with normal audiometric thresholds, we measured speech recognition performance in a spatial listening task with two interfering talkers (speech-in-speech identification), audiometric thresholds, binaural sensitivity to the temporal fine structure (interaural phase differences [IPD]), serial memory with and without interfering talkers, and self-reported noise sensitivity. Speech-in-speech processing was not significantly associated with the ISE. The most important predictors of high speech-in-speech recognition performance were a large short-term memory span, low IPD thresholds, bilaterally symmetrical audiometric thresholds, and low individual noise sensitivity. Surprisingly, the susceptibility of short-term memory to irrelevant sound accounted for a substantially smaller amount of variance in speech-in-speech processing than the nondisrupted short-term memory capacity. The data confirm the role of binaural sensitivity to the temporal fine structure, although its association to SiN recognition was weaker than in some previous studies. The inverse association between self-reported noise sensitivity and SiN processing deserves further investigation.

Interaural time differences are often considered a weak cue for stream segregation. We investigated this claim with headphone-presented pure tones differing in a related form of interaural configuration-interaural phase differences (ΔIPD)-or/and in frequency (ΔF). In experiment 1, sequences comprised 5 × ABA- repetitions (A and B = 80-ms tones, "-" = 160-ms silence), and listeners reported whether integration or segregation was heard. Envelope shape was varied but remained constant across all tones within a trial. Envelopes were either quasi-trapezoidal or had a fast attack and slow release (FA-SR) or vice versa (SA-FR). The FA-SR envelope caused more segregation than SA-FR in a task where only ΔIPD cues were present, but not in a corresponding ΔF-only task. In experiment 2, interstimulus interval (ISI) was varied (0-60 ms) between FA-SR tones. ΔF-based segregation decreased with increasing ISI, whereas ΔIPD-based segregation increased. This suggests that binaural temporal integration may limit segregation at short ISIs. In another task, ΔF and ΔIPD cues were presented alone or in combination. Here, ΔIPD-based segregation was greatly reduced, suggesting ΔIPD-based segregation is highly sensitive to experimental context. Experiments 1-2 demonstrate that ΔIPD can promote segregation in optimized stimuli/tasks. Experiment 3 employed a task requiring integration for good performance. Listeners detected a delay on the final four B tones of an 8 × ABA- sequence. Although performance worsened with increasing ΔF, increasing ΔIPD had only a marginal impact. This suggests that, even in stimuli optimized for ΔIPD-based segregation, listeners remained mostly able to disregard ΔIPD when segregation was detrimental to performance.

In the intricate acoustic landscapes where speech intelligibility is challenged by noise and reverberation, multichannel speech enhancement emerges as a promising solution for individuals with hearing loss. Such algorithms are commonly evaluated at the utterance scale. However, this approach overlooks the granular acoustic nuances revealed by phoneme-specific analysis, potentially obscuring key insights into their performance. This paper presents an in-depth phoneme-scale evaluation of three state-of-the-art multichannel speech enhancement algorithms. These algorithms-filter-and-sum network, minimum variance distortionless response, and Tango-are here extensively evaluated across different noise conditions and spatial setups, employing realistic acoustic simulations with measured room impulse responses, and leveraging diversity offered by multiple microphones in a binaural hearing setup. The study emphasizes the fine-grained phoneme-scale analysis, revealing that while some phonemes like plosives are heavily impacted by environmental acoustics and challenging to deal with by the algorithms, others like nasals and sibilants see substantial improvements after enhancement. These investigations demonstrate important improvements in phoneme clarity in noisy conditions, with insights that could drive the development of more personalized and phoneme-aware hearing aid technologies. Additionally, while this study provides extensive data on the physical metrics of processed speech, these physical metrics do not necessarily imitate human perceptions of speech, and the impact of the findings presented would have to be investigated through listening tests.

Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods struggle to meet both high accuracy and interpretability. We propose a deep learning model called the auditory decoding transformer (ADT) network for speech envelope reconstruction from EEG signals to address these issues. The ADT network uses spatio-temporal convolution for feature extraction, followed by a transformer decoder to decode the speech envelopes. Through anticausal masking, the ADT considers only the current and future EEG features to match the natural relationship of speech and EEG. Performance evaluation shows that the ADT network achieves average reconstruction scores of 0.168 and 0.167 on the SparrKULee and DTU datasets, respectively, rivaling those of other nonlinear models. Furthermore, by visualizing the weights of the spatio-temporal convolution layer as time-domain filters and brain topographies, combined with an ablation study of the temporal convolution kernels, we analyze the behavioral patterns of the ADT network in decoding speech envelopes. The results indicate that low- (0.5-8 Hz) and high-frequency (14-32 Hz) EEG signals are more critical for envelope reconstruction and that the active brain regions are primarily distributed bilaterally in the auditory cortex, consistent with previous research. Visualization of attention scores further validated previous research. In summary, the ADT network balances high performance and interpretability, making it a promising tool for studying neural speech envelope tracking.

Speech-recognition tests are widely used in both clinical and research audiology. The purpose of this study was the development of a novel speech-recognition test that combines concepts of different speech-recognition tests to reduce training effects and allows for a large set of speech material. The new test consists of four different words per trial in a meaningful construct with a fixed structure, the so-called phrases. Various free databases were used to select the words and to determine their frequency. Highly frequent nouns were grouped into thematic categories and combined with related adjectives and infinitives. After discarding inappropriate and unnatural combinations, and eliminating duplications of (sub-)phrases, a total number of 772 phrases remained. Subsequently, the phrases were synthesized using a text-to-speech system. The synthesis significantly reduces the effort compared to recordings with a real speaker. After excluding outliers, measured speech-recognition scores for the phrases with 31 normal-hearing participants at fixed signal-to-noise ratios (SNR) revealed speech-recognition thresholds (SRT) for each phrase varying up to 4 dB. The median SRT was -9.1 dB SNR and thus comparable to existing sentence tests. The psychometric function's slope of 15 percentage points per dB is also comparable and enables efficient use in audiology. Summarizing, the principle of creating speech material in a modular system has many potential applications.

While listening, we commonly participate in simultaneous activities. For instance, at receptions people often stand while engaging in conversation. It is known that listening and postural control are associated with each other. Previous studies focused on the interplay of listening and postural control when the speech identification task had rather high cognitive control demands. This study aimed to determine whether listening and postural control interact when the speech identification task requires minimal cognitive control, i.e., when words are presented without background noise, or a large memory load. This study included 22 young adults, 27 middle-aged adults, and 21 older adults. Participants performed a speech identification task (auditory single task), a postural control task (posture single task) and combined postural control and speech identification tasks (dual task) to assess the effects of multitasking. The difficulty levels of the listening and postural control tasks were manipulated by altering the level of the words (25 or 30 dB SPL) and the mobility of the platform (stable or moving). The sound level was increased for adults with a hearing impairment. In the dual-task, listening performance decreased, especially for middle-aged and older adults, while postural control improved. These results suggest that even when cognitive control demands for listening are minimal, interaction with postural control occurs. Correlational analysis revealed that hearing loss was a better predictor than age of speech identification and postural control.

During the last decade, there has been a move towards consumer-centric hearing healthcare. This is a direct result of technological advancements (e.g., merger of consumer grade hearing aids with consumer grade earphones creating a wide range of hearing devices) as well as policy changes (e.g., the U.S. Food and Drug Administration creating a new over-the-counter [OTC] hearing aid category). In addition to various direct-to-consumer (DTC) hearing devices available on the market, there are also several validated tools for the self-assessment of auditory function and the detection of ear disease, as well as tools for education about hearing loss, hearing devices, and communication strategies. Further, all can be made easily available to a wide range of people. This perspective provides a framework and identifies tools to improve and maintain optimal auditory wellness across the adult life course. A broadly available and accessible set of tools that can be made available on a digital platform to aid adults in the assessment and as needed, the improvement, of auditory wellness is discussed.

Loudness is a fundamental dimension of auditory perception. When hearing impairment results in a loudness deficit, hearing aids are typically prescribed to compensate for this. However, the relationship between an individual's specific hearing impairment and the hearing aid fitting strategy used to address it is usually not straightforward. Various iterations of fine-tuning and troubleshooting by the hearing care professional are required, based largely on experience and the introspective feedback from the hearing aid user. We present the development of a new method for validating an individual's loudness perception of natural signals relative to a normal-hearing reference. It is a measurement method specifically designed for the situation typically encountered by hearing care professionals, namely, with hearing-impaired individuals in the free field with their hearing aids in place. In combination with the qualitative user feedback that the measurement is fast and that its results are intuitively displayed and easily interpretable, the method fills a gap between existing tools and is well suited to provide concrete guidance and orientation to the hearing care professional in the process of individual gain adjustment.

Timing cues such as interaural time differences (ITDs) and temporal pitch are pivotal for sound localization and source segregation, but their perception is degraded in cochlear-implant (CI) listeners as compared to normal-hearing listeners. In multi-electrode stimulation, intra-aural channel interactions between electrodes are assumed to be an important factor limiting access to those cues. The monaural asynchrony of stimulation timing across electrodes is assumed to mediate the amount of these interactions. This study investigated the effect of the monaural temporal electrode asynchrony (mTEA) between two electrodes, applied similarly in both ears, on ITD-based left/right discrimination sensitivity in five CI listeners, using pulse trains with 100 pulses per second and per electrode. Forward-masked spatial tuning curves were measured at both ears to find electrode separations evoking controlled degrees of across-electrode masking. For electrode separations smaller than 3 mm, results showed an effect of mTEA. Patterns were u/v-shaped, consistent with an explanation in terms of the effective pulse rate that appears to be subject to the well-known rate limitation in electric hearing. For separations larger than 7 mm, no mTEA effects were observed. A comparison to monaural rate-pitch discrimination in a separate set of listeners and in a matched setup showed no systematic differences between percepts. Overall, an important role of the mTEA in both binaural and monaural dual-electrode stimulation is consistent with a monaural pulse-rate limitation whose effect is mediated by channel interactions. Future CI stimulation strategies aiming at improved timing-cue encoding should minimize the stimulation delay between nearby electrodes that need to be stimulated successively.