Evidence accumulates that the cerebellum\'s role in the brain is not restricted to motor functions. Rather, cerebellar activity seems to be crucial for a variety of tasks that rely on precise event timing and prediction. Due to its complex structure and importance in communication, human speech requires a particularly precise and predictive coordination of neural processes to be successfully comprehended. Recent studies proposed that the cerebellum is indeed a major contributor to speech processing, but how this contribution is achieved mechanistically remains poorly understood. The current study aimed to reveal a mechanism underlying cortico-cerebellar coordination and demonstrate its speech-specificity. In a reanalysis of magnetoencephalography data, we found that activity in the cerebellum aligned to rhythmic sequences of noise-vocoded speech, irrespective of its intelligibility. We then tested whether these \"entrained\" responses persist, and how they interact with other brain regions, when a rhythmic stimulus stopped and temporal predictions had to be updated. We found that only intelligible speech produced sustained rhythmic responses in the cerebellum. During this \"entrainment echo,\" but not during rhythmic speech itself, cerebellar activity was coupled with that in the left inferior frontal gyrus, and specifically at rates corresponding to the preceding stimulus rhythm. This finding represents evidence for specific cerebellum-driven temporal predictions in speech processing and their relay to cortical regions.

BACKGROUND: Automatic speech recognition (ASR) can potentially help older adults and people with disabilities reduce their dependence on others and increase their participation in society. However, maxillectomy patients with reduced speech intelligibility may encounter some problems using such technologies.
OBJECTIVE: To investigate the accuracy of three commonly used ASR platforms when used by Japanese maxillectomy patients with and without their obturator placed.
METHODS: Speech samples were obtained from 29 maxillectomy patients with and without their obturator and 17 healthy volunteers. The samples were input into three speaker-independent speech recognition platforms and the transcribed text was compared with the original text to calculate the syllable error rate (SER). All participants also completed a conventional speech intelligibility test to grade their speech using Taguchi\'s method. A comprehensive articulation assessment of patients without their obturator was also performed.
RESULTS: Significant differences in SER were observed between healthy and maxillectomy groups. Maxillectomy patients with an obturator showed a significant negative correlation between speech intelligibility scores and SER. However, for those without an obturator, no significant correlations were observed. Furthermore, for maxillectomy patients without an obturator, significant differences were found between syllables grouped by vowels. Syllables containing /i/, /u/ and /e/ exhibited higher error rates compared to those containing /a/ and /o/. Additionally, significant differences were observed when syllables were grouped by consonant place of articulation and manner of articulation.
CONCLUSIONS: The three platforms performed well for healthy volunteers and maxillectomy patients with their obturator, but the SER for maxillectomy patients without their obturator was high, rendering the platforms unusable. System improvement is needed to increase accuracy for maxillectomy patients.

Understanding speech in noisy environments is a challenging task, especially in communication situations with several competing speakers. Despite their ongoing improvement, assistive listening devices and speech processing approaches still do not perform well enough in noisy multi-talker environments, as they may fail to restore the intelligibility of a speaker of interest among competing sound sources. In this study, a quasi-causal deep learning algorithm was developed that can extract the voice of a target speaker, as indicated by a short enrollment utterance, from a mixture of multiple concurrent speakers in background noise. Objective evaluation with computational metrics demonstrated that the speaker-informed algorithm successfully extracts the target speaker from noisy multi-talker mixtures. This was achieved using a single algorithm that generalized to unseen speakers, different numbers of speakers and relative speaker levels, and different speech corpora. Double-blind sentence recognition tests on mixtures of one, two, and three speakers in restaurant noise were conducted with listeners with normal hearing and listeners with hearing loss. Results indicated significant intelligibility improvements with the speaker-informed algorithm of 17% and 31% for people without and with hearing loss, respectively. In conclusion, it was demonstrated that deep learning-based speaker extraction can enhance speech intelligibility in noisy multi-talker environments where uninformed speech enhancement methods fail.

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.

Since its creation, the coordinate response measure (CRM) corpus has been applied in hundreds of studies to explore the mechanisms of informational masking in multi-talker situations, but also in speech-in-noise or auditory attentional tasks. Here, we present its French version, with equivalent content to the original version in English. Furthermore, an evaluation of speech-on-speech intelligibility in French shows informational masking with similar result patterns to the original data in English. This validation of the French CRM corpus allows to propose the use of the CRM for intelligibility tests in French, and for comparisons with a foreign language under masking conditions.

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.

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.

When investigating unobservable, complex traits, data collection and aggregation processes can introduce distinctive features to the data such as boundedness, measurement error, clustering, outliers, and heteroscedasticity. Failure to collectively address these features can result in statistical challenges that prevent the investigation of hypotheses regarding these traits. This study aimed to demonstrate the efficacy of the Bayesian beta-proportion generalized linear latent and mixed model (beta-proportion GLLAMM) (Rabe-Hesketh et al., Psychometrika, 69(2), 167-90, 2004a, Journal of Econometrics, 128(2), 301-23, 2004c, 2004b; Skrondal and Rabe-Hesketh 2004) in handling data features when exploring research hypotheses concerning speech intelligibility. To achieve this objective, the study reexamined data from transcriptions of spontaneous speech samples initially collected by Boonen et al. (Journal of Child Language, 50(1), 78-103, 2023). The data were aggregated into entropy scores. The research compared the prediction accuracy of the beta-proportion GLLAMM with the normal linear mixed model (LMM) (Holmes et al., 2019) and investigated its capacity to estimate a latent intelligibility from entropy scores. The study also illustrated how hypotheses concerning the impact of speaker-related factors on intelligibility can be explored with the proposed model. The beta-proportion GLLAMM was not free of challenges; its implementation required formulating assumptions about the data-generating process and knowledge of probabilistic programming languages, both central to Bayesian methods. Nevertheless, results indicated the superiority of the model in predicting empirical phenomena over the normal LMM, and its ability to quantify a latent potential intelligibility. Additionally, the proposed model facilitated the exploration of hypotheses concerning speaker-related factors and intelligibility. Ultimately, this research has implications for researchers and data analysts interested in quantitatively measuring intricate, unobservable constructs while accurately predicting the empirical phenomena.

Although the telephone band (0.3-3 kHz) provides sufficient information for speech recognition, the contribution of the non-telephone band (<0.3 and >3 kHz) is unclear. To investigate its contribution, speech intelligibility and talker identification were evaluated using consonants, vowels, and sentences. The non-telephone band produced relatively good intelligibility for consonants (76.0%) and sentences (77.4%), but not vowels (11.5%). The non-telephone band supported good talker identification only with sentences (74.5%), but not vowels (45.8%) or consonants (10.8%). Furthermore, the non-telephone band cannot produce satisfactory speech intelligibility in noise at the sentence level, suggesting the importance of full-band access in realistic listening.

UNASSIGNED: Communication breakdowns and their repair by deaf and hard-of-hearing (DHH) and hearing adolescents were examined in conversation with an unfamiliar communication partner.
UNASSIGNED: This study compared the number and type of clarification requests and responses to those requests of 16 DHH and 16 normal-hearing adolescents aged 11-16 years, in a 10-minute conversation with an unfamiliar adult. It also analyzed the relationship between speech intelligibility, communication breakdowns, and clarification requests by an unfamiliar adult. the Children\'s Communication Checklist (CCC) was completed by parents.
UNASSIGNED: DHH adolescents demonstrated significantly higher usage of nonverbal clarification requests and verbal and nonverbal responses to clarification requests compared to normal-hearing adolescents in conversations with an unfamiliar adult. Furthermore, the subscale scores of the CCC and the speech intelligibility of DHH adolescents were significantly lower than those of normal-hearing adolescents. There were correlations between speech intelligibility and the speech subscale score of the CCC, as well as correlations between the pragmatic composite score of the CCC, the number of communication breakdowns, and the number of clarification requests by an unfamiliar adult.
UNASSIGNED: The adolescents with DHH experienced more communication breakdowns in conversation with an unfamiliar adult and the number of clarification requests made by adults was higher.