Human speech evolution is not just about having a speech-ready brain and vocal apparatus.

When a person listens to natural speech, the relation between features of the speech signal and the corresponding evoked electroencephalogram (EEG) is indicative of neural processing of the speech signal. Using linguistic representations of speech, we investigate the differences in neural processing between speech in a native and foreign language that is not understood. We conducted experiments using three stimuli: a comprehensible language, an incomprehensible language, and randomly shuffled words from a comprehensible language, while recording the EEG signal of native Dutch-speaking participants. We modeled the neural tracking of linguistic features of the speech signals using a deep-learning model in a match-mismatch task that relates EEG signals to speech, while accounting for lexical segmentation features reflecting acoustic processing. The deep learning model effectively classifies coherent versus nonsense languages. We also observed significant differences in tracking patterns between comprehensible and incomprehensible speech stimuli within the same language. It demonstrates the potential of deep learning frameworks in measuring speech understanding objectively.

OBJECTIVE: Sound pressure and exhaled flow have been identified as important factors associated with higher particle emissions. The aim of this study was to assess how different vocalizations affect the particle generation independently from other factors.
METHODS: Experimental study.
METHODS: Thirty-three experienced singers repeated two different sentences in normal loudness and whispering. The first sentence consisted mainly of consonants like /k/ and /t/ as well as open vowels, while the second sentence also included the /s/ sound and contained primarily closed vowels. The particle emission was measured using condensation particle counter (CPC, 3775 TSI Inc.) and aerodynamic particle sizer (APS, 3321 TSI Inc.). The CPC measured particle number concentration for particles larger than 4 nm and mainly reflects the number of particles smaller than 0.5 µm since these particles dominate total number concentration. The APS measured particle size distribution and number concentration in the size range of 0.5-10 µm and data were divided into >1 µm and <1 µm particle size ranges. Generalized linear mixed-effects models were constructed to assess the factors affecting particle generation.
RESULTS: Whispering produced more particles than speaking and sentence 1 produced more particles than sentence 2 while speaking. Sound pressure level had effect on particle production independently from vocalization. The effect of exhaled airflow was not statistically significant.
CONCLUSIONS: Based on our results the type of vocalization has a significant effect on particle production independently from other factors such as sound pressure level.

BACKGROUND: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.
METHODS: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.
RESULTS: On the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.
CONCLUSIONS: In a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.).

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.

We sought to derive an objective measure of psychomotor slowing from speech analytics during a psychiatric interview to avoid potential burden of dedicated neurophysiological testing. Speech latency, which reflects response time between speakers, shows promise from the literature. Speech data was obtained from 274 subjects with a diagnosis of bipolar I depression enrolled in a randomized, doubleblind, 6-week phase 2 clinical trial. Audio recordings of structured Montgomery-Åsberg Depression Rating Scale (MADRS) interviews at 6 time points were examined (k = 1,352). We evaluated speech latencies, and other aspects of speech, for temporal stability, convergent validity, sensitivity/responsivity to clinical change, and generalization across seven socio-linguistically diverse countries. Speech latency was minimally associated with demographic features, and explained nearly a third of the variance in depression (categorically defined). Speech latency significantly decreased as depression symptoms improved over time, explaining nearly 20 % of variance in depression remission. Classification for differentiating people with versus without concurrent depression was high (AUCs > 0.85) both cross-sectionally and longitudinally. Results replicated across countries. Other speech features offered modest incremental contribution. Neurophysiological speech parameters with face validity can be derived from psychiatric interviews without the added patient burden of additional testing.

OBJECTIVE: The Corollary Discharge (CD) mechanism inhibits self-generated speech sound perception, appearing disrupted in schizophrenia and potentially contributing to Anomalous Self-Experiences (ASEs). However, it remains unclear if this alteration and its correlation with ASEs extend to other psychotic disorders.
METHODS: Electroencephalography was used to study the N1 Event-Related Potential (ERP) as an index of CD-mediated suppression in the auditory cortex across thirty-five participants with schizophrenia, twenty-six with bipolar disorder, and thirty healthy controls. Auditory N1 was elicited by two conditions: real-time listening to self-pronounced vowels while speaking through connected microphone and earphones (listen/talk -or talk condition in previous literature-) and passive listening to the same previously recorded self-uttered vowels (listen/no talk -or listen condition-).
RESULTS: N1 ERP amplitude was lower in the listen/talk condition compared to listen/no talk across all groups. However, N1 suppression was significantly reduced in schizophrenia, with bipolar patients showing intermediate attenuation between both groups (i.e., non-significantly different from controls). Furthermore, N1 suppression inversely correlated with ASEs severity only in schizophrenia.
CONCLUSIONS: Dysfunction of the CD mechanism may be a defining feature of schizophrenia, where it is connected to ASEs.
CONCLUSIONS: These results corroborate previous findings linking auditory N1 ERP suppression with disrupted CD mechanism in schizophrenia, but not in bipolar disorder.

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.

暂无摘要。

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.