UNASSIGNED: The consensus in scientific literature is that each child undergoes a unique linguistic development path, albeit with shared developmental stages. Some children excel or lag behind their peers in language skills. Consequently, a key challenge in language acquisition research is pinpointing factors influencing individual differences in language development.
UNASSIGNED: We observed children longitudinally from 3 to 24 months of life to explore early predictors of vocabulary size. Based on the productive vocabulary size of children at 24 months, 30 children met our sample selection criteria: 10 late talkers and 10 early talkers, and we compared them with 10 typical talkers. We evaluated interactive behaviors at 3, 6, 9 and 12 months, considering vocal production, gaze at mother\'s face, and gestural production during mother-child interactions, and we considered mothers\' report of children\'s actions and gestures and receptive-vocabulary size at 15 and 18 months.
UNASSIGNED: Results indicated early precursors of language outcome at 24 months identifiable as early as 3 months in vocal productions, 6 months for gaze at mother\'s face and 12 months for gestural productions.
UNASSIGNED: Our research highlights both theoretical and practical implications. Theoretically, identifying the early indicators of belonging to the group of late or early talkers underscores the significant role of this developmental period for future studies. On a practical note, our findings emphasize the crucial need for early investigations to identify predictors of vocabulary development before the typical age at which lexical delay is identified.

UNASSIGNED: Identifying hate speech (HS) is a central concern within online contexts. Current methods are insufficient for efficient preemptive HS identification. In this study, we present the results of an analysis of automatic HS identification applied to popular alt-right YouTube videos.
UNASSIGNED: This essay describes methodological challenges of automatic HS detection. The case study concerns data on a formative segment of contemporary radical right discourse. Our purpose is twofold. (1) To outline an interdisciplinary mixed-methods approach for using automated identification of HS. This bridges the gap between technical research on the one hand (such as machine learning, deep learning, and natural language processing, NLP) and traditional empirical research on the other. Regarding alt-right discourse and HS, we ask: (2) What are the challenges in identifying HS in popular alt-right YouTube videos?
UNASSIGNED: The results indicate that effective and consistent identification of HS communication necessitates qualitative interventions to avoid arbitrary or misleading applications. Binary approaches of hate/non-hate speech tend to force the rationale for designating content as HS. A context-sensitive qualitative approach can remedy this by bringing into focus the indirect character of these communications. The results should interest researchers within social sciences and the humanities adopting automatic sentiment analysis and for those analysing HS and radical right discourse.
UNASSIGNED: Automatic identification or moderation of HS cannot account for an evolving context of indirect signification. This study exemplifies a process whereby automatic hate speech identification could be utilised effectively. Several methodological steps are needed for a useful outcome, with both technical quantitative processing and qualitative analysis being vital to achieve meaningful results. With regard to the alt-right YouTube material, the main challenge is indirect framing. Identification demands orientation in the broader discursive context and the adaptation towards indirect expressions renders moderation and suppression ethically and legally precarious.

BACKGROUND: Psychosis involves a distortion of thought content, which is partly reflected in anomalous ways in which words are semantically connected into utterances in speech. We sought to explore how these linguistic anomalies are realized through putative circuit-level abnormalities in the brain\'s semantic network.
METHODS: Using a computational large-language model, Bidirectional Encoder Representations from Transformers (BERT), we quantified the contextual expectedness of a given word sequence (perplexity) across 180 samples obtained from descriptions of 3 pictures by patients with first-episode schizophrenia (FES) and controls matched for age, parental social status, and sex, scanned with 7 T ultra-high field functional magnetic resonance imaging (fMRI). Subsequently, perplexity was used to parametrize a spectral dynamic causal model (DCM) of the effective connectivity within (intrinsic) and between (extrinsic) 4 key regions of the semantic network at rest, namely the anterior temporal lobe, the inferior frontal gyrus (IFG), the posterior middle temporal gyrus (MTG), and the angular gyrus.
RESULTS: We included 60 participants, including 30 patients with FES and 30 controls. We observed higher perplexity in the FES group, indicating that speech was less predictable by the preceding context among patients. Results of Bayesian model comparisons showed that a DCM including the group by perplexity interaction best explained the underlying patterns of neural activity. We observed an increase of self-inhibitory effective connectivity within the IFG, as well as reduced self-inhibitory tone within the pMTG, in the FES group. An increase in self-inhibitory tone in the IFG correlated strongly and positively with inter-regional excitation between the IFG and posterior MTG, while self-inhibition of the posterior MTG was negatively correlated with this interregional excitation.
CONCLUSIONS: Our design did not address connectivity in the semantic network during tasks that selectively activated the semantic network, which could corroborate findings from this resting-state fMRI study. Furthermore, we do not present a replication study, which would ideally use speech in a different language.
CONCLUSIONS: As an explanation for peculiar speech in psychosis, these results index a shift in the excitatory-inhibitory balance regulating information flow across the semantic network, confined to 2 regions that were previously linked specifically to the executive control of meaning. Based on our approach of combining a large language model with causal connectivity estimates, we propose loss in semantic control as a potential neurocognitive mechanism contributing to disorganization in psychosis.

Patients suffering from Parkinson\'s disease suffer from voice impairment. In this study, we introduce models to classify normal and Parkinson\'s patients using their speech. We used an AST (audio spectrogram transformer), a transformer-based speech classification model that has recently outperformed CNN-based models in many fields, and a CNN-based PSLA (pretraining, sampling, labeling, and aggregation), a high-performance model in the existing speech classification field, for the study. This study compares and analyzes the models from both quantitative and qualitative perspectives. First, qualitatively, PSLA outperformed AST by more than 4% in accuracy, and the AUC was also higher, with 94.16% for AST and 97.43% for PSLA. Furthermore, we qualitatively evaluated the ability of the models to capture the acoustic features of Parkinson\'s through various CAM (class activation map)-based XAI (eXplainable AI) models such as GradCAM and EigenCAM. Based on PSLA, we found that the model focuses well on the muffled frequency band of Parkinson\'s speech, and the heatmap analysis of false positives and false negatives shows that the speech features are also visually represented when the model actually makes incorrect predictions. The contribution of this paper is that we not only found a suitable model for diagnosing Parkinson\'s through speech using two different types of models but also validated the predictions of the model in practice.

Speech alterations have been reported in manifest Huntington\'s disease (HD) and premanifest mutation carriers (preHD). The aim of our study was to explore these alterations in preHD and whether they can be used as biomarkers. 13 preHD mutation carriers performed reading task, sustained phonation task and syllable repetition tasks at baseline and after 21 months, as well as clinical examination and MRI. Syllable repetition capacity and self-chosen velocity of single syllable repetition differed significantly between time points. There were no changes in clinical ratings or MRI volumetry. Measurements of speech might be sensitive tools for monitoring subclinical changes in preHD.

OBJECTIVE: This study aimed to compare the influence of four different maxillary removable orthodontic retainers on speech.
METHODS: Eligibility criteria for sample selection were: 20-40-year subjects with acceptable occlusion, native speakers of Portuguese. The volunteers (n=21) were divided in four groups randomized with a 1:1:1:1 allocation ratio. The four groups used, in random order, the four types of retainers full-time for 21 days each, with a washout period of 7-days. The removable maxillary retainers were: conventional wraparound, wraparound with an anterior hole, U-shaped wraparound, and thermoplastic retainer. Three volunteers were excluded. The final sample comprised 18 subjects (11 male; 7 female) with mean age of 27.08 years (SD=4.65). The speech evaluation was performed in vocal excerpts recordings made before, immediately after, and 21 days after the installation of each retainer, with auditory-perceptual and acoustic analysis of formant frequencies F1 and F2 of the vowels. Repeated measures ANOVA and Friedman with Tukey tests were used for statistical comparison.
RESULTS: Speech changes increased immediately after conventional wraparound and thermoplastic retainer installation, and reduced after 21 days, but not to normal levels. However, this increase was statistically significant only for the wraparound with anterior hole and the thermoplastic retainer. Formant frequencies of vowels were altered at initial time, and the changes remained in conventional, U-shaped and thermoplastic appliances after three weeks.
CONCLUSIONS: The thermoplastic retainer was more harmful to the speech than wraparound appliances. The conventional and U-shaped retainers interfered less in speech. The three-week period was not sufficient for speech adaptation.

UNASSIGNED: Previous studies have suggested that inspirations during speech pauses are influenced by the length of adjacent utterances, owing to respiratory motor planning and physiological recovery processes. The goal of this study was to examine how attention to respiratory sensations may influence these processes in aging speakers with dyspnea, by measuring the effect of sensory monitoring on the relationship between utterance length and the occurrence of inspirations, as well as on functional voice and respiratory measures.
UNASSIGNED: Seventeen adults aged 50 years and older with complaints of voicing-related dyspnea completed a repeated-measures protocol consisting of a 2-week baseline phase and a 4-week sensory monitoring phase. Audiovisual recordings of semistructured speech and self-report questionnaires were collected at study onset, after the baseline phase, and after the sensory monitoring phase. Repeated-measures logistic regressions were conducted to examine changes in the relationship between utterance length and the occurrence of inspirations in adjacent pauses, and repeated-measures analyses of variance were used to investigate any changes in functional voice and respiratory measures.
UNASSIGNED: Planning and recovery processes appeared to remain constant across the baseline phase. From postbaseline to postsensory monitoring timepoints, a strengthening of the relationship between the presence of an inspiration during a speech pause and the length of the subsequent-but not preceding-utterance was noted. Significant improvements were noted in voice-related handicap from study onset to postsensory monitoring, but no changes were reported in respiratory comfort during speech.
UNASSIGNED: Results suggest that respiratory planning processes, that is, the ability to plan breath intakes based on the length of upcoming utterances, may be modifiable behaviorally through targeted sensory monitoring. Further studies are warranted to validate the proposed role of respiratory sensation awareness in achieving skilled temporal coordination between voicing and breathing.

Separable input and output phonological working memory (WM) capacities have been proposed, with the input capacity supporting speech recognition and the output capacity supporting production. We examined the role of input vs. output phonological WM in narrative production, examining speech rate and pronoun ratio - two measures with prior evidence of a relation to phonological WM. For speech rate, a case series approach with individuals with aphasia found no significant independent contribution of input or output phonological WM capacity after controlling for single-word production. For pronoun ratio, there was some suggestion of a role for input phonological WM. Thus, neither finding supported a specific role for an output phonological buffer in speech production. In contrast, two cases demonstrating dissociations between input and output phonological WM capacities provided suggestive evidence of predicted differences in narrative production, though follow-up research is needed. Implications for case series vs. case study approaches are discussed.

Objective.Speech brain-computer interfaces (BCIs) have the potential to augment communication in individuals with impaired speech due to muscle weakness, for example in amyotrophic lateral sclerosis (ALS) and other neurological disorders. However, to achieve long-term, reliable use of a speech BCI, it is essential for speech-related neural signal changes to be stable over long periods of time. Here we study, for the first time, the stability of speech-related electrocorticographic (ECoG) signals recorded from a chronically implanted ECoG BCI over a 12 month period.Approach.ECoG signals were recorded by an ECoG array implanted over the ventral sensorimotor cortex in a clinical trial participant with ALS. Because ECoG-based speech decoding has most often relied on broadband high gamma (HG) signal changes relative to baseline (non-speech) conditions, we studied longitudinal changes of HG band power at baseline and during speech, and we compared these with residual high frequency noise levels at baseline. Stability was further assessed by longitudinal measurements of signal-to-noise ratio, activation ratio, and peak speech-related HG response magnitude (HG response peaks). Lastly, we analyzed the stability of the event-related HG power changes (HG responses) for individual syllables at each electrode.Main Results.We found that speech-related ECoG signal responses were stable over a range of syllables activating different articulators for the first year after implantation.Significance.Together, our results indicate that ECoG can be a stable recording modality for long-term speech BCI systems for those living with severe paralysis.Clinical Trial Information.ClinicalTrials.gov, registration number NCT03567213.

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.