Greek uses H*, L + H*, and H* + L, all followed by L-L% edge tones, as nuclear pitch accents in statements. A previous analysis demonstrated that these accents are distinguished by F0 scaling and contour shape. This study expands the earlier investigation by exploring additional cues, namely, voice quality, amplitude, and duration, in distinguishing the pitch accents, and investigating individual variability in the selection of both F0 and non-F0 cues. Bayesian multivariate analysis and hierarchical clustering demonstrate that the accents are distinguished not only by F0 but also by additional cues at the group level, with individual variability in cue selection.

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

By 2050, 1 in 4 people worldwide will be living with hearing impairment. We propose a digital Speech Hearing Screener (dSHS) using short nonsense word recognition to measure speech-hearing ability. The importance of hearing screening is increasing due to the anticipated increase in individuals with hearing impairment globally. We compare dSHS outcomes with standardized pure-tone averages (PTA) and speech-recognition thresholds (SRT). Fifty participants (aged 55 or older underwent pure-tone and speech-recognition thresholding. One-way ANOVA was used to compare differences between hearing impaired and hearing not-impaired groups, by the dSHS, with a clinical threshold of moderately impaired hearing at 35 dB and severe hearing impairment at 50 dB. dSHS results significantly correlated with PTAs/SRTs. ANOVA results revealed the dSHS was significantly different (F(1,47) = 38.1, p < 0.001) between hearing impaired and unimpaired groups. Classification analysis using a 35 dB threshold, yielded accuracy of 85.7% for PTA-based impairment and 81.6% for SRT-based impairment. At a 50 dB threshold, dSHS classification accuracy was 79.6% for PTA-based impairment (Negative Predictive Value (NPV)-93%) and 83.7% (NPV-100%) for SRT-based impairment. The dSHS successfully differentiates between hearing-impaired and unimpaired individuals in under 3 min. This hearing screener offers a time-saving, in-clinic hearing screening to streamline the triage of those with likely hearing impairment to the appropriate follow-up assessment, thereby improving the quality of services. Future work will investigate the ability of the dSHS to help rule out hearing impairment as a cause or confounder in clinical and research applications.

Across the animal kingdom, neural responses in the auditory cortex are suppressed during vocalization, and humans are no exception. A common hypothesis is that suppression increases sensitivity to auditory feedback, enabling the detection of vocalization errors. This hypothesis has been previously confirmed in non-human primates, however a direct link between auditory suppression and sensitivity in human speech monitoring remains elusive. To address this issue, we obtained intracranial electroencephalography (iEEG) recordings from 35 neurosurgical participants during speech production. We first characterized the detailed topography of auditory suppression, which varied across superior temporal gyrus (STG). Next, we performed a delayed auditory feedback (DAF) task to determine whether the suppressed sites were also sensitive to auditory feedback alterations. Indeed, overlapping sites showed enhanced responses to feedback, indicating sensitivity. Importantly, there was a strong correlation between the degree of auditory suppression and feedback sensitivity, suggesting suppression might be a key mechanism that underlies speech monitoring. Further, we found that when participants produced speech with simultaneous auditory feedback, posterior STG was selectively activated if participants were engaged in a DAF paradigm, suggesting that increased attentional load can modulate auditory feedback sensitivity.
The brain lowers its response to inputs we generate ourselves, such as moving or speaking. Essentially, our brain ‘knows’ what will happen next when we carry out these actions, and therefore does not need to react as strongly as it would to unexpected events. This is why we cannot tickle ourselves, and why the brain does not react as much to our own voice as it does to someone else’s. Quieting down the brain’s response also allows us to focus on things that are new or important without getting distracted by our own movements or sounds. Studies in non-human primates showed that neurons in the auditory cortex (the region of the brain responsible for processing sound) displayed suppressed levels of activity when the animals made sounds. Interestingly, when the primates heard an altered version of their own voice, many of these same neurons became more active. But it was unclear whether this also happens in humans. To investigate, Ozker et al. used a technique called electrocorticography to record neural activity in different regions of the human brain while participants spoke. The results showed that most areas of the brain involved in auditory processing showed suppressed activity when individuals were speaking. However, when people heard an altered version of their own voice which had an unexpected delay, those same areas displayed increased activity. In addition, Ozker et al. found that the higher the level of suppression in the auditory cortex, the more sensitive these areas were to changes in a person’s speech. These findings suggest that suppressing the brain’s response to self-generated speech may help in detecting errors during speech production. Speech deficits are common in various neurological disorders, such as stuttering, Parkinson’s disease, and aphasia. Ozker et al. hypothesize that these deficits may arise because individuals fail to suppress activity in auditory regions of the brain, causing a struggle when detecting and correcting errors in their own speech. However, further experiments are needed to test this theory.

A test is proposed to characterize the performance of speech recognition systems. The QuickSIN test is used by audiologists to measure the ability of humans to recognize continuous speech in noise. This test yields the signal-to-noise ratio at which individuals can correctly recognize 50% of the keywords in low-context sentences. It is argued that a metric for automatic speech recognizers will ground the performance of automatic speech-in-noise recognizers to human abilities. Here, it is demonstrated that the performance of modern recognizers, built using millions of hours of unsupervised training data, is anywhere from normal to mildly impaired in noise compared to human participants.

A speech intelligibility (SI) prediction model is proposed that includes an auditory preprocessing component based on the physiological anatomy and activity of the human ear, a hierarchical spiking neural network, and a decision back-end processing based on correlation analysis. The auditory preprocessing component effectively captures advanced physiological details of the auditory system, such as retrograde traveling waves, longitudinal coupling, and cochlear nonlinearity. The ability of the model to predict data from normal-hearing listeners under various additive noise conditions was considered. The predictions closely matched the experimental test data under all conditions. Furthermore, we developed a lumped mass model of a McGee stainless-steel piston with the middle-ear to study the recovery of individuals with otosclerosis. We show that the proposed SI model accurately simulates the effect of middle-ear intervention on SI. Consequently, the model establishes a model-based relationship between objective measures of human ear damage, like distortion product otoacoustic emissions, and speech perception. Moreover, the SI model can serve as a robust tool for optimizing parameters and for preoperative assessment of artificial stimuli, providing a valuable reference for clinical treatments of conductive hearing loss.

In everyday acoustic environments, reverberation alters the speech signal received at the ears. Normal-hearing listeners are robust to these distortions, quickly recalibrating to achieve accurate speech perception. Over the past two decades, multiple studies have investigated the various adaptation mechanisms that listeners use to mitigate the negative impacts of reverberation and improve speech intelligibility. Following the PRISMA guidelines, we performed a systematic review of these studies, with the aim to summarize existing research, identify open questions, and propose future directions. Two researchers independently assessed a total of 661 studies, ultimately including 23 in the review. Our results showed that adaptation to reverberant speech is robust across diverse environments, experimental setups, speech units, and tasks, in noise-masked or unmasked conditions. The time course of adaptation is rapid, sometimes occurring in less than 1 s, but this can vary depending on the reverberation and noise levels of the acoustic environment. Adaptation is stronger in moderately reverberant rooms and minimal in rooms with very intense reverberation. While the mechanisms underlying the recalibration are largely unknown, adaptation to the direct-to-reverberant ratio-related changes in amplitude modulation appears to be the predominant candidate. However, additional factors need to be explored to provide a unified theory for the effect and its applications.

BACKGROUND: Speech sounds are processed in the human brain through intricate and interconnected cortical and subcortical structures. Two neural signatures, one largely from cortical sources (mismatch response, MMR) and one largely from subcortical sources (frequency-following response, FFR) are critical for assessing speech processing as they both show sensitivity to high-level linguistic information. However, there are distinct prerequisites for recording MMR and FFR, making them difficult to acquire simultaneously NEW METHOD: Using a new paradigm, our study aims to concurrently capture both signals and test them against the following criteria: (1) replicating the effect that the MMR to a native speech contrast significantly differs from the MMR to a nonnative speech contrast, and (2) demonstrating that FFRs to three speech sounds can be reliably differentiated.
RESULTS: Using EEG from 18 adults, we observed a decoding accuracy of 72.2% between the MMR to native vs. nonnative speech contrasts. A significantly larger native MMR was shown in the expected time window. Similarly, a significant decoding accuracy of 79.6% was found for FFR. A high stimulus-to-response cross-correlation with a 9ms lag suggested that FFR closely tracks speech sounds.
CONCLUSIONS: These findings demonstrate that our paradigm reliably captures both MMR and FFR concurrently, replicating and extending past research with much fewer trials (MMR: 50 trials; FFR: 200 trials) and shorter experiment time (12minutes).
CONCLUSIONS: This study paves the way to understanding cortical-subcortical interactions for speech and language processing, with the ultimate goal of developing an assessment tool specific to early development.

Past studies have explored formant centering, a corrective behavior of convergence over the duration of an utterance toward the formants of a putative target vowel. In this study, we establish the existence of a similar centering phenomenon for pitch in healthy elderly controls and examine how such corrective behavior is altered in Alzheimer\'s Disease (AD). We found the pitch centering response in healthy elderly was similar when correcting pitch errors below and above the target (median) pitch. In contrast, patients with AD showed an asymmetry with a larger correction for the pitch errors below the target phonation than above the target phonation. These findings indicate that pitch centering is a robust compensation behavior in human speech. Our findings also explore the potential impacts on pitch centering from neurodegenerative processes impacting speech in AD.

Real-world evidence is increasingly used to support clinical and regulatory decisions globally and may be a useful tool to study the unique needs of cochlear implant users in China. The ability to recognize and understand speech in noise is critical for cochlear implant users, however, this remains a challenge in everyday settings with fluctuating competing noise levels. The Cochlear™ Sound Processor, Nucleus® 7 (CP1000), includes Forward Focus, a spatial noise algorithm aimed to improve speech-in-noise performance, and Made for iPhone/iPod/iPad functionality. We conducted a prospective, single-center, open-label, within-participant, real-world evidence investigation in participants with cochlear implants. The primary objective of this study, conducted in China, was to compare speech perception in spatially separated dynamic noise with the Nucleus 7 to the recipients\' current older Cochlear Sound Processor, including the Freedom and Nucleus 5 sound processors. A follow-up study monitored participants from the initial study up to 12-months post the fitting of their Nucleus 7 and investigated hearing ability, satisfaction, and usability of the device via a questionnaire. Forty participants were included in the initial study (age-range 3 to 49 years) and 29 continued to the follow-up study (age-range 5 to 28 years). The participants were heterogeneous in terms of age, cochlear implant experience, and duration of hearing loss. Nucleus 7 significantly improved participant speech recognition performance in noise by 7.54 dB when compared with the participants\' current older sound processor (p<0.0001). Overall satisfaction with Nucleus 7 was 72%. Satisfaction in different hearing contexts ranged from 93.1% for understanding a 1:1 conversation in a quiet setting, 62.1% for understanding on the phone, to 34.5% hearing in complex noisy situations. The study demonstrated the benefits of the Nucleus 7 sound processor across different hearing environments in a Chinese population and showed improved hearing ability, usability, and satisfaction in a real-world every-day environment.