Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal, and fast-progressive neurodegenerative disease characterized by the degeneration of motor neurons. ALS patients often experience an initial misdiagnosis or a diagnostic delay due to the current unavailability of an efficient biomarker. Since impaired speech is typical in ALS, we hypothesized that functional differences between healthy and ALS participants during speech tasks can be explained by cortical pattern changes, thereby leading to the identification of a neural biomarker for ALS. In this pilot study, we collected magnetoencephalography (MEG) recordings from three early-diagnosed patients with ALS and three healthy controls during imagined (covert) and overt speech tasks. First, we computed sensor correlations, which showed greater correlations for speakers with ALS than healthy controls. Second, we compared the power of the MEG signals in canonical bands between the two groups, which showed greater dissimilarity in the beta band for ALS participants. Third, we assessed differences in functional connectivity, which showed greater beta band connectivity for ALS than healthy controls. Finally, we performed single-trial classification, which resulted in highest performance with beta band features (∼ 98%). These findings were consistent across trials, phrases, and participants for both imagined and overt speech tasks. Our preliminary results indicate that speech-evoked beta oscillations could be a potential neural biomarker for diagnosing ALS. To our knowledge, this is the first demonstration of the detection of ALS from single-trial neural signals.

BACKGROUND: Responsive deep brain stimulation (rDBS) uses physiological signals to deliver stimulation when needed. rDBS is hypothesized to reduce stimulation-induced speech effects associated with continuous DBS (cDBS) in patients with essential tremor (ET).
OBJECTIVE: To determine if rDBS reduces cDBS speech-related side effects while maintaining tremor suppression.
METHODS: Eight ET participants with thalamic DBS underwent unilateral rDBS. Both speech evaluations and tremor severity were assessed across three conditions (DBS OFF, cDBS ON, and rDBS ON). Speech was analyzed using intelligibility ratings. Tremor severity was scored using the Fahn-Tolosa-Marin Tremor Rating Scale (TRS).
RESULTS: During unilateral cDBS, participants experienced reduced speech intelligibility (P = 0.025) compared to DBS OFF. rDBS was not associated with a deterioration of intelligibility. Both rDBS (P = 0.026) and cDBS (P = 0.038) improved the contralateral TRS score compared to DBS OFF.
CONCLUSIONS: rDBS maintained speech intelligibility without loss of tremor suppression. A larger prospective chronic study of rDBS in ET is justified. © 2024 International Parkinson and Movement Disorder Society.

BACKGROUND: Vocal biomarkers, derived from acoustic analysis of vocal characteristics, offer noninvasive avenues for medical screening, diagnostics, and monitoring. Previous research demonstrated the feasibility of predicting type 2 diabetes mellitus through acoustic analysis of smartphone-recorded speech. Building upon this work, this study explores the impact of audio data compression on acoustic vocal biomarker development, which is critical for broader applicability in health care.
OBJECTIVE: The objective of this research is to analyze how common audio compression algorithms (MP3, M4A, and WMA) applied by 3 different conversion tools at 2 bitrates affect features crucial for vocal biomarker detection.
METHODS: The impact of audio data compression on acoustic vocal biomarker development was investigated using uncompressed voice samples converted into MP3, M4A, and WMA formats at 2 bitrates (320 and 128 kbps) with MediaHuman (MH) Audio Converter, WonderShare (WS) UniConverter, and Fast Forward Moving Picture Experts Group (FFmpeg). The data set comprised recordings from 505 participants, totaling 17,298 audio files, collected using a smartphone. Participants recorded a fixed English sentence up to 6 times daily for up to 14 days. Feature extraction, including pitch, jitter, intensity, and Mel-frequency cepstral coefficients (MFCCs), was conducted using Python and Parselmouth. The Wilcoxon signed rank test and the Bonferroni correction for multiple comparisons were used for statistical analysis.
RESULTS: In this study, 36,970 audio files were initially recorded from 505 participants, with 17,298 recordings meeting the fixed sentence criteria after screening. Differences between the audio conversion software, MH, WS, and FFmpeg, were notable, impacting compression outcomes such as constant or variable bitrates. Analysis encompassed diverse data compression formats and a wide array of voice features and MFCCs. Wilcoxon signed rank tests yielded P values, with those below the Bonferroni-corrected significance level indicating significant alterations due to compression. The results indicated feature-specific impacts of compression across formats and bitrates. MH-converted files exhibited greater resilience compared to WS-converted files. Bitrate also influenced feature stability, with 38 cases affected uniquely by a single bitrate. Notably, voice features showed greater stability than MFCCs across conversion methods.
CONCLUSIONS: Compression effects were found to be feature specific, with MH and FFmpeg showing greater resilience. Some features were consistently affected, emphasizing the importance of understanding feature resilience for diagnostic applications. Considering the implementation of vocal biomarkers in health care, finding features that remain consistent through compression for data storage or transmission purposes is valuable. Focused on specific features and formats, future research could broaden the scope to include diverse features, real-time compression algorithms, and various recording methods. This study enhances our understanding of audio compression\'s influence on voice features and MFCCs, providing insights for developing applications across fields. The research underscores the significance of feature stability in working with compressed audio data, laying a foundation for informed voice data use in evolving technological landscapes.

UNASSIGNED: The domination of the Contemporary Commercial Music (CCM) industry in music markets has led to a significant increase in the number of CCM performers. Performing in a wide variety of singing styles involves exposing CCM singers to specific risk factors potentially leading to voice problems. This, in turn, necessitates the consideration of this particular group of voice users in the Occupational Health framework. The aim of the present research was threefold. First, it sought to profile the group of Polish CCM singers. Second, it was designed to explore the prevalence of self-reported voice problems and voice quality in this population, in both speech and singing. Third, it aimed to explore the relationships between voice problems and lifetime singing involvement, occupational voice use, smoking, alcohol consumption, vocal training, and microphone use, as potential voice risk factors.
UNASSIGNED: The study was conducted in Poland from January 2020 to April 2023. An online survey included socio-demographic information, singing involvement characteristics, and singers\' voice self-assessment. The prevalence of voice problems was assessed by the Polish versions of the Vocal Tract Discomfort Scale (VTDS) and the Singing Voice Handicap Index (SVHI). Also, a self-reported dysphonia symptoms protocol was applied. The perceived overall voice quality was assessed by a Visual Analogue Scale (VAS) of 100 mm.
UNASSIGNED: 412 singers, 310 women and 102 men, completed the survey. Nearly half of the studied population declared lifetime singing experience over 10 years with an average daily singing time of 1 or 2 h. 283 participants received vocal training. For 11.4% of respondents, singing was the primary income source, and 42% defined their career goals as voice-related. The median scores of the VTDS were 11.00 (0-44) and 12.00 (0-40) for the Frequency and Severity subscales, respectively. The median SVHI score of 33 (0-139) was significantly higher than the normative values determined in a systematic review and meta-analysis (2018). Strong positive correlations were observed between SVHI and both VTD subscales: Frequency (r = 0.632, p < 0.001) and Severity (r = 0.611, p < 0.001). The relationships between most of the other variables studied were weak or negligible.
UNASSIGNED: The examined CCM singers exhibited substantial diversity with regard to musical genre preferences, aspirations pertaining to singing endeavors, career affiliations, and source of income. Singing voice assessment revealed a greater degree of voice problems in the examined cohort than so far reported in the literature, based on the SVH and VTDS.

English is widely regarded as a global language, and it has become increasingly important for global communication. As a result, the demand for English language education has been on the rise. In China, a significant number of individuals are engaged in learning the English language. However, many English learners in China encounter challenges when it comes to developing their speaking skills. This study aims to investigate the factors influencing the speaking skills of English learners in China. Employing a mixed-methods approach, data were collected through a questionnaire from 455 college students from three different courses (arts, science & business, and commerce) in China. The study findings identified several factors impacting the speaking skills of English learners in China, including limited opportunities for speaking practice, fear of making mistakes, limited exposure to English-speaking environments, inadequate teacher training, and the influence of the Chinese language on English pronunciation. Additionally, the study highlighted that learners who have greater exposure to English-speaking environments and more opportunities for speaking practice tend to demonstrate better speaking skills. The novelty of this study lies in its valuable insights into the factors influencing the speaking skills of English learners in China. Based on the findings, it is recommended that English teachers receive enhanced training to effectively teach speaking skills, and learners should be provided with increased opportunities for speaking practice, such as participating in group discussions or engaging in speaking activities.

BACKGROUND: Tooth loss has been associated with cognitive decline, but the underlying mechanisms involving speech and psychosocial impairment remain unclear.
OBJECTIVE: To investigate the impact of tooth loss-related speech and psychosocial impairment on cognitive function in Hong Kong\'s older population.
METHODS: Seventy-six Cantonese-speaking participants between the ages of 51-92 were classified into three groups: patients with complete dentures (CD), partially edentulous patients with less than 10 occluding tooth pairs (OU <10), and at least 10 occluding tooth pairs (OU ≥10). Cognitive function was assessed using the Montreal Cognitive Assessment Hong Kong Version, One-minute Verbal Fluency Task and Hayling Sentence Completion Test. Objective and subjective speech assessments were carried out using artificial intelligence speech recognition algorithm and a self-designed speech questionnaire. The impact of tooth loss on psychosocial condition was evaluated by the Reading the Mind in the Eyes Test and a self-designed questionnaire. Statistical analyses (one-way ANOVA, ANCOVA, Kruskal-Wallis test, Spearman correlation test) were performed.
RESULTS: Tooth loss was significantly associated with lower cognitive function (p = .008), speech accuracy (p = .018) and verbal fluency (p = .001). Correlations were found between cognitive function and speech accuracy (p < .0001). No significant difference in tooth loss-related psychosocial impact was found between the three groups.
CONCLUSIONS: While warranting larger sample sizes, this pilot study highlights the need for further research on the role of speech in the association between tooth loss and cognitive function. The potential cognitive impact of tooth retention, together with its known biological and proprioceptive benefits, supports the preservation of the natural dentition.

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

A previous study discovered that two speakers with moderate apraxia of speech increased their sequential motion rates after unilateral forced-nostril breathing (UFNB) practiced as an adjunct to speech-language therapy in an AB repeated-measures design. The current study sought to: (1) delineate possible UFNB plus practice effects from practice effects alone in motor speech skills; (2) examine the relationships between UFNB integrity, participant-reported stress levels, and motor speech performance; and (3) sample a participant-led UFNB training schedule to contribute to the literature\'s growing understanding of UFNB dosage. A single-subject (n-of-1 trial), ABAB reversal design was used across four motor speech behaviors. A 60-year-old female with chronic, severe apraxia of speech participated. The researchers developed a breathing app to assess UFNB practice integrity and administer the Simple Aphasia Stress Scale after each UFNB session. The participant improved from overall severe to moderate apraxia of speech on the Apraxia Battery for Adults. Visual inspection of graphs confirmed robust motor speech practice effects for all variables. Articulatory-kinematic variables demonstrated sensitivity to the UFNB-plus-practice condition and correlated to stress scale scores but not UFNB integrity scores. The participant achieved 20-minute UFNB sessions 4 times per week. Removal of UFNB during A2 (UFNB withdrawal) and after a 10-day break during B2 (UFNB full dosage) revealed UFNB practice effects on stress scale scores. UFNB with motor speech practice may benefit articulatory-kinematic skills compared to motor speech practice alone. Regular, cumulative UFNB practice appeared to lower self-perceived stress levels. These findings, along with prior work, provide a foundation to further explore yoga breathing and its use with speakers who have apraxia of speech.

BACKGROUND: Children born with a cleft palate with or without a cleft lip (CP/L) are at increased risk for delayed language development and speech sound disorders. Enhanced Milieu Teaching with Phonological Emphasis (EMT+PE) is a recommended naturalistic intervention for toddlers with CP/L. The parents\' role in providing naturalistic interventions is critical and they need training based on learning principles to implement these interventions. Telepractice is an appropriate method for training parents and children with various speech-related disorders.
OBJECTIVE: This study aims to determine and compare the effectiveness of telepractice and the parent-implemented EMT+PE intervention on language and speech measures in toddlers with CP/L with usual interventions and determine the effectiveness maintenance of the intervention.
METHODS: A randomized controlled trial (RCT) will assess the efficacy of telepractice and the parent-implemented EMT+PE intervention in enhancing speech and language measures in toddlers with CP/L. Eligible participants will be randomly assigned to one of 2 groups: the conventional intervention group and the EMT+PE intervention group. Participants\' speech and language measures will be evaluated remotely by trained raters before and after the intervention and 2 months after the intervention. Parents of participants in the intervention group will receive 3 months of training in speech and language supportive strategies from trained therapists using telehealth fidelity scales. Parents of participants in the control group will receive the conventional speech and language intervention by cleft team therapists. Study outcomes will include language variables (mean length of utterance) and speech production variables (percent correct consonants).
RESULTS: The protocol was approved by the Research Ethics Committee of the University of Social Welfare and Rehabilitation Sciences in February 2022. The selection process of participants, as well as training therapists and raters, commenced in January 2022, the therapy and follow-up period ended in June 2023, and pre- and postintervention assessments have been conducted. Data analysis is ongoing, and we expect to publish our results by the summer of 2024. Funding is yet to be received.
CONCLUSIONS: The results of this study may help us develop a speech and language intervention with a different delivery model for toddlers with CP/L, and the cleft team care can use these results in service delivery. Consistent with our hypothesis, speech and language measures are expected to improve.
UNASSIGNED: DERR1-10.2196/54426.

Objective. In recent years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.Approach. To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-nearest-neighbors (kNNs) and support vector machine (SVM).Main results. The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.Significance. This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.