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In today's society, the need for functional communication is essential from an early age. However, many children struggle with speech, language, and communication during their time in early childhood education and care (ECEC). This paper examines early childhood special education teachers' (ECSETs) views on how children with speech, language and communication needs are supported in regular ECEC. The empirical data comprised four focus group interviews with 14 itinerant ECSETs. A thematic analysis was used to examine the teachers' support strategies. The following four themes related to the research question were identified after an iterative process of coding and close reading of data: (a) cooperation and professionalism as prerequisites for providing support; (b) processes and procedures for providing support; (c) consultation for personnel enabling support for children in a group; and (d) adjusting support based on each child's individual needs. The study provides insight into providing support for children with speech, language and communication needs and related challenges. The results indicate several areas that work well in providing support during specific activities and for certain group compositions. However, potential pitfalls include ECSETs' workload and lack of knowledge and motivation among personnel to support children in need of support. [ABSTRACT FROM AUTHOR]

This retrospective study investigated the influence of palatal fistula (PF) formation after double opposing Z-plasty (DOZ) on speech outcomes in patients with cleft palate (CP), focusing on cleft width and palatal length as predictors of velopharyngeal insufficiency (VPI). This study included 1117 patients with CP (579 males, 538 females) who underwent DOZ, performed by a single surgeon, between 1988 and 2017. Demographic characteristics, cleft dimensions, history of PF formation, and speech outcomes were investigated. Speech evaluations were performed at a minimum age of five to assess nasal emission, hypernasality, compensatory articulation, intelligibility, necessity for VPI surgery, and speech therapy. Logistic regression analysis was performed. Speech assessments were conducted at the median age of five (interquartile range [IQR], 5–6 years). Overall, 96.5% of patients achieved 'socially acceptable speech' after DOZ. Patients with PF history showed greater cleft width and experienced higher rates of hypernasality, nasal emission, and VPI on videofluoroscopy (VFS) compared to those without PF history (mean, 11.4 mm vs. 7.1 mm; 28.4% vs. 23.6%; 34.8% vs. 14.9%, 38.5% vs. 14.0%, 40.6% vs. 28.3%, respectively; all p < 0.0001). Cleft width was significantly associated with VPI-related speech outcomes in the multivariate logistic regression analysis, affecting both perceptual and VFS-measured outcomes. A wider CP gap significantly increased the risk of VPI-related speech difficulties after DOZ. Cleft width is a more critical predictor of adverse speech outcomes than the presence of small-to-medium-sized PFs. Patients with a history of PF and wider cleft gaps require targeted interventions and intensified follow-up to effectively manage and improve speech outcomes. [ABSTRACT FROM AUTHOR]

This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco's Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paperbased assessments in improving reading skills among 100 Moroccan first to third-graders. The mixedmethod approach combined pre-post standardized reading tests with qualitative feedback. Results showed students receiving the AI-enabled speech recognition assessments demonstrated significant gains in reading achievement compared to peers assessed via traditional methods. Qualitative findings revealed benefits of instant feedback and enhanced engagement provided by the speech recognition tool. This study contributes timely empirical evidence on adopting learning technologies, specifically AI-driven automated speech assessment instruments, to enhance foundational literacy development within under-resourced education systems implementing student-centered pedagogical techniques like TaRL. It provides valuable insights and guidance for integrating innovative speech analysis tools within localized teaching and learning frameworks to strengthen early reading instruction and monitoring. [ABSTRACT FROM AUTHOR]

This article explores Shakespeare's use of coined words in his plays and poems. It discusses the Shakespeare Birthplace Trust's claim that Shakespeare used over 1,700 words that were not previously recorded in English, although other sources suggest the number may be higher. The article examines the methods Shakespeare used to create these words and the debate over whether he truly invented them or popularized existing ones. It also explores the influence of Baldassare Castiglione's book "The Courtier" on Shakespeare's works, suggesting that Castiglione's concept of Sprezzatura may have influenced Shakespeare's use of language. The article concludes by discussing the connection between Castiglione's work and the authorship debate, suggesting that Edward de Vere may be the true author of Shakespeare's plays. [Extracted from the article]

Background: Several medical diagnoses generate unpleasant smells, which cause suffering for patients. There is no culturally accepted language for malodour, and the topic is encircled by silence in nursing. Aim: To facilitate communication with patients on unpleasant smell. Method: A linguistic analysis of verbal expressions from 11 patients suffering from malodour was conducted. Discourse-based metaphor theory guided the analysis. Findings: Metaphors of comparison and orientation were the most common categories of metaphors. The comparisons referred to bodily waste products and rotting processes. Metaphors of orientation illuminated how the unpleasant smell moved around and settled down in the body and in their homes without the patients' control. Swearing was used to convey suffering. Conclusion: The figures of speech provided new insight into how malodour affected the patients existentially by threatening social relations, freedom and integrity. Recognizing the patients verbal expressions may act as a starting point for communication. [ABSTRACT FROM AUTHOR]

This article intends to focus on some of the possibilities for analysis and reflection that emerge from the reading of I, Pierre Rivière, having slaughtered my mother, my sister, my brother: a 1973 text, edited by Foucault, which develops from the recognition of the potency inherent in the act of speech by the speechless. Pierre Rivière is in fact considered the one who, through but also beyond his terrible deed, has the (entirely political) ability to take the risk of "challenging power." It is precisely by means of this act that he undertakes a process of desubjection and subjectivation, imposing disruptive and scandalous truths and discourses against other truths and discourses recognized as dominant and more authoritative. Pierre Rivière's Memoir cannot therefore be investigated as a confession; rather, it has to do with parrhēsia, anticipating many of the Foucauldian reflections on the subject, which would not be developed until several years later. Moreover, it does not really concern an isolated individual. The subject Rivière speaks of is one who not only rises up for his own part but also paves the way for the many without a part, thus outlining the possibilities of constructing a collective "we" that aims to conquer a political space. From here the question arises: "Who, in our present, might the Pierre Rivières be?" A question that has nothing to do with the tragic facts of the parricide but which allows us to explore what Pierre Rivière enables us to think and say today. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Electrónica Interuniversitaria De Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We investigate the well studied Pretérito Perfeito Composto (tenho cantado) and the rather under-studied vir + gerúndio periphrasis (venho cantando) in Brazilian Portuguese based on the working hypothesis that they represent functionally highly similar but competing constructions. Aiming at identifying linguistic factors that impact the choice between them, the theoretical and corpus-based analysis discovers a diverging treatment of the underlying eventuality's potential to be continued after the speech time, as well as a contrary preference concerning morphological number of involved arguments. Within a broader research context, this contributes new evidence concerning the current status of the Pretérito Perfeito Composto in Brazilian Portuguese. While it has been argued that it tends be displaced more and more by the Simple Past, the present study reveals that the vir + gerúndio periphrasis is another emerging competitor. [ABSTRACT FROM AUTHOR]

In laboratory studies, speakers often modify their speech (e.g., by hyper-articulating, attempting a "supra-regional" norm, or hypercorrecting) to 'speak correctly', especially under the (perceived) expectation of formal speech. A strategy to address this challenge involves comparing performance on one variable against a well-documented pattern on another variable. Previous research in Spanish shows /s/ sensitivity to speech formality, making it a useful comparison for probing other lenition processes. This study uses the acoustics of coda /s/ to predict the acoustics of /n/ and pre-nasal vowels among speakers of Argentine and Dominican Spanish, two dialects in which these have been widely attested. The data analyzed come from 28 speakers of Dominican Spanish and 26 of Argentine Spanish, recorded with a nasometer (a split channel set of microphones that record nose and mouth signals separately). Findings shows pervasive presence of frication for /s/ (whether [s] or [h]), suggesting heightened attention to speech. Additionally, longer /s/ co-occurs with longer nasal consonants. However, when duration of /s/ was compared to the time-course of nasalization, only the Argentine data showed earlier onset of nasalization with shorter /s/. The addition of a well-known sociolinguistic variable can serve as a validity measure for a less understood variable. [ABSTRACT FROM AUTHOR]

Deep convolutional neural network (CNN) has been widely used in speech recognition technology. The model based on deep CNN can effectively improve the quality of human-computer interaction. However, the existing CNN with fixed convolutional kernel size has a disadvantage on extracting data features. It is hard to effectively identify whether the extracted features sufficient or not. As a result, a self-tuning convolutional kernel (STCK) algorithm is proposed to solve the mentioned problem. Firstly, the computational process of STCK algorithm is derived. Then the calculation formula of the convolutional kernel size is obtained. Meanwhile, Bark-spectrum is introduced to extract the spectrogram of speech signal, which is used as the CNN input to adapt to the human hearing. In addition, the data enhancement strategies are proposed, namely frame channel shielding and Bark-band channel shielding. The presented strategies can further improve the generalization ability of the recognition model. The experimental results show that, compared with another two models (the CNN model without STCK algorithm and the CNN model without the data enhancement strategy), the training loss of the proposed method is minimum. And the recognition error rates for the test samples are reduced by 3.9% and 1%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of English Teaching & Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the abstraction and complexity of speech data, accurately recognizing human emotions through speech is a very challenging task. A difficult problem to solve in many previous studies is the severe misclassification of several specific emotions, such as "Happy" and "Sad". In this paper, we propose a dual-path framework with Multi-Head Bayesian Coattention (MHBA) for speech emotion recognition under human-machine interaction (HMI), which consists of the context emotional feature extraction branch and the long-term prosody information supplement branch. Specifically, in the context emotional feature extraction branch, we firstly select two specific transformer blocks of pre-trained Wav2vec2.0 to extract emotional representation with rich short-term acoustic information as short-term emotional representation. Furthermore, we employ the United Attention Network (UAN) to selectively discover target emotion regions from representation, wherein Context-aware Attention Pooling mechanism (CAAP) capturing the contextual dependencies of spatiotemporal features. Meanwhile, in the long-term prosody information supplement branch, we utilize the long-term prosody feature as supplementary input to complement the missing prosody emotional information in the Wav2vec2.0 feature. Finally, to effectively integrate information from both branches, we enhance the coattention with the Bayesian Attention Module (BAM), which estimates prior distributions using emotion-relevant knowledge. Our approach has been evaluated on the benchmark dataset IEMOCAP, yielding promising experimental results. Specifically, we observed significant improvements in both weighted accuracy(WA) and unweighted accuracy(UA). Our approach achieves an absolute increase of 3.68% in WA and 0.49% in UA, further validating its effectiveness and performance. [ABSTRACT FROM AUTHOR]

This paper presents an innovative, custom Hindi Speech Corpus dedicated to assessing the positive intensity in Hindi language. By meticulously gathering voice samples from individuals across diverse demographics, this corpus is well crafted for acoustic research in Sentiment Analysis. Several sentences spanning a spectrum of sentiments (extremely positive, positive, neutral, negative) were acquired from participants. The resulting corpus comprises of 225 audio files. To guarantee the consistency and to accommodate domain knowledge based nuances, the sentiments and its intensity (high, medium, low) were classified by Expert annotation. We used PowerBI for attaining insights into emotional landscape of the corpus. Employing exploratory analysis and Sentiment Analysis, the corpus is analyzed to unveil linguistic features indicative of positive intensity in Hindi speech. This study advances the understanding of positivity expression in Hindi and furnishes valuable resources for linguistic investigations in the scope of Hindi language. It is observed that positive sentiment has the highest count at 58 and the lowest count is for very negative at 27. The main contribution of current work lies in creating the custom dataset in Hindi which to our knowledge is first of its kind. Also, we did not come across any dataset focused on intensity of positivity. These contributions shall definitely be beneficial to future researchers in exploring positive affect related research. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, intelligent cockpit has increasingly become a trend in on-board related equipment. The intelligent cockpit system can not only provide auxiliary support for users in the driving environment, but also greatly improve driving efficiency. With the advancement of technology and the improvement of people's material lives, people's demand for driving environment safety and intelligence is gradually increasing. In order to effectively enhance the interactivity of the intelligent cockpit and ensure the safety of users' driving, this article effectively analyzed the performance and functional requirements of the intelligent cockpit, and combined speech recognition algorithms to construct an artificial intelligence (AI) speech interaction system. On this basis, this paper provides an in-depth study of intelligent cockpit design. To verify the effectiveness of the intelligent cockpit application, it was tested from four aspects: system operation accuracy, command response time, system security, and user experience. The test findings showed that at the level of system command response time, the response time of driving assistance commands, entertainment media commands, and traffic information commands was basically concentrated below 300 milliseconds. From the specific test results, it can be seen that the intelligent cockpit based on the artificial intelligence voice interaction system can quickly respond to users' different command needs through precise speech recognition. [ABSTRACT FROM AUTHOR]

We investigate two novel predictors of review helpfulness: grammatical classes of words, such as verbs and articles, and the arrays they form, or syntax. Patterns of grammar and syntax are known to co-occur with speech varieties that affect communication outcomes. However, measuring text syntax can be methodologically challenging. To address this, we analyze ordered word positions within reviews rather than full texts and look for text segments where the diversity of unique words and classes of words peaks. We find that the five-word segment at the beginning of a review, which we call the opening, exhibits these characteristics. Through statistical modeling and content analyses, we show that grammar and syntax classes in the opening predict review helpfulness and co-occur with specific clusters of words in the full text. Additionally, experimental studies provide evidence that the opening does not work in isolation, supporting the assumption that consumers read reviews in full. These findings may help to simplify online review analyses and inform future research agendas on consumer reviews. [ABSTRACT FROM AUTHOR]

Introduction: Maternal speech has been shown to benefit premature infants by improving feeding outcomes and potentiating the development of the auditory cortex. To our knowledge, limited studies have addressed the benefits of exposure to maternal speech on neural development in premature infants. The objective of this study was to investigate the effect of early controlled exposure to maternal recording a passage of speech on heart rate variability (obtained before, during, and after playback of a test stimulus of a female stranger speaking the same passage) in very low-birth-weight premature infants tracked weekly from 28 to 34 weeks. Methods: Prospective, randomized controlled trial, longitudinal, and repeated-measures design were conducted on 49 subjects. Infants heard a recording of their mother's speech twice a day from either 28 to 34 weeks (group 1) or from 32 to 34 weeks (group 2). Spectral analysis was measured weekly for 45 seconds before, during, and after playback of maternal speech. A generalized linear mixed model was conducted to examine the 2-way interaction in the log high-frequency power between groups, genders, sessions, and conditions. Results: It was found that there were no significant differences between groups before, during, and after playback of the stimulus. A significant difference, however, was noted between conditions (before vs during period). Conclusion: It can be concluded cautiously that playing back of maternal speech recordings to the premature infant has a beneficial impact on neural development after 32 weeks gestational age. [ABSTRACT FROM AUTHOR]

This review aims to provide a correlation between oral and oropharyngeal subsites and type of reconstruction used in the management of head and neck cancer patients. A literature search of PubMed, Embase and Web of Science was conducted. All study types describing long-term speech and swallow outcomes of adults following head and neck oncological reconstruction, which used a subsite classification, were included. Risk of bias was assessed using the Robbins-1 tool. A total of 2270 patients were found in 26 studies. The number of subsites/studies ranged from 2 to 18. Subsites were predominantly divided on an anatomical basis. Other classifications included functionally grouped subsites. Seven articles considered combinations, unilateral and bilateral defects. Base of tongue, FOM, and defects crossing the midline are negatively correlated with post-operative speech and swallow. Lateral distributions were associated with superior outcomes. The University of Washington Quality of Life Questionnaire (UW-QOL) was the most prevalent tool for speech and swallow assessment. Other factors that significantly affect speech and swallow outcomes include adjuvant therapy, size, type of reconstruction (free flap compared to pedicled or local). The role of neoadjuvant therapy remains unknown. A consistent and formalised approach including risk stratification for multiple contributing factors would be useful in clinical pre- and post-operative management. [ABSTRACT FROM AUTHOR]

GLUT1 deficiency syndrome (GLUT1DS) is a rare genetic disorder caused by a mutation in the SLC2A1 gene that limits the transport of glucose across the blood-brain barrier. Speech disorders and dysarthria are typical findings in patients with GLUT1DS, but have never been deeply phenotyped. The aim of the present study was to characterize speech abilities in a sample of patients with GLUT1DS. 30 patients with GLUT1DS were recruited. We reported impairments in different speech and oromotor domains: the speech was characterized by dysarthria, inaccurate articulation of consonants, abnormal nasal resonance, errors in intonation and prosody and low intelligibility. We observed difficulties in motor planning and programming. Moreover, we observed a significant difference between the dysarthric level of impairment with genotype groups. The presence of a speech disorder in patients with GLUT1DS represents a core feature of the syndrome. Our findings suggest that patients with GLUT1DS would benefit from a comprehensive neurocognitive assessment to detect strengths and weaknesses of the speech profile. Understanding the speech and language phenotype in GLUT1DS is critical for planning early intervention to positively influence the global development of patients with GLUT1DS. • The purpose of this study is to analyze and characterize the speech profile of a sample of patients with GLUT1DS. • The majority of the patients manifested dysarthria with different levels of severity. • A relationship between genotype and dysartric level of impairment was found. • The introduction of the ketogenic diet therapy might be a protective factor for cognitive outcomes in patients with GLUT1DS. [ABSTRACT FROM AUTHOR]

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, the etiology of which has not been clearly determined yet. There is increasing evidence that synaptic and dendritic changes are involved in the etiology of ASD. The aim of this study is to determine whether serum Thrombospondin-1 and Thrombospondin-2 differ between ASD patients and healthy controls. This study also investigates possible correlations between clinical symptomatology of ASD and serum Thrombospondin-1 and Thrombospondin-2 levels. Method: A total of 44 children with ASD and 21 healthy controls under 6 years of age were included in the study. Symptom severity and behavioral problems among children with ASD were evaluated by using Childhood Autism Rating Scale and Abnormal Behavior Checklist. Serum levels of Thrombospondin-1 and Thrombospondin-2 were measured by using commercial enzyme-linked immunosorbent assay kits. Result: No statistically significant differences were found between the two groups in terms of serum Thrombospondin-1 and Thrombospondin-2 levels. In addition, no correlation was determined between Thrombospondin-2 levels and clinical symptomatology and severity of ASD. However, the Thrombospondin-1 level was found to negatively correlated with the total score of Childhood Autism Rating Scale, inappropriate speech and stereotype subscale scores of Aberrant Behavior Checklist scale. Conclusion: Thrombospondin-1 might have a potential role in the etiopathogenesis of ASD. Further studies are required to clearly elucidate the association between Trombospondin-1 and ASD. [ABSTRACT FROM AUTHOR]

Speech translation technology is an important booster for promoting social communication and advancing human civilization. With the solid advancement of theories and technologies such as speech processing and machine translation, as well as the continuous deepening and development of computer science, the computing power and storage capacity have been further improved. Speech translation systems widely used in English, French, English and Chinese have successively reached a commercial level. However, the development of speech translation systems is limited by corpus resources and a lack of bilingual language research. The experiments and applications of speech translation in some languages are still in its early stages. In addition, existing research mostly adopts a cascading voice translation system as the basis, breaking through the key issues individually, and less adopts direct voice translation methods without relying on intermediate text representation, which brings problems such as cumbersome research content, complex translation models, and long translation delays. This article mainly focuses on the design of a translation error correction system based on improved Seq2Seq. Firstly, relevant research is summarized, relevant application methods are proposed, and the results are discussed. I hope to bring some inspiration to relevant researchers and provide assistance for the development of related fields. [ABSTRACT FROM AUTHOR]

This study assumes that former President Rodrigo Duterte and President Ferdinand Marcos Jr. used different types of deixis in their first State of the Nation Address (SONAs). Supporting this assumption is the theory of Deictic Expressions by Stephen Levinson. This study is descriptive qualitative. Findings revealed that former President Duterte and President Marcos Jr. used different types of deixis in their speeches. The most dominant type of deixis used by former President Duterte is personal deixis, which obtained the highest frequency, particularly the pronoun "I". In contrast, the most prevalent deixis used by President Marcos is "personal deixis", particularly the pronoun "we". It is concluded that personal deixis was the most dominant type of deixis among the five types used by the two speakers in their speeches. Future researchers are encouraged to conduct a future study that will focus on the usage of deixis in books or speeches. [ABSTRACT FROM AUTHOR]

In this paper, we present a method that integrates a Grammar Transducer as an external language model to enhance the accuracy of the pre-trained Korean End-to-end (E2E) Automatic Speech Recognition (ASR) model. The E2E ASR model utilizes the Connectionist Temporal Classification (CTC) loss function to derive hypothesis sentences from input audio. However, this method reveals a limitation inherent in the CTC approach, as it fails to capture language information from transcript data directly. To overcome this limitation, we propose a fusion approach that combines a clause-level n-gram language model, transformed into a Weighted Finite-State Transducer (WFST), with the E2E ASR model. This approach enhances the model's accuracy and allows for domain adaptation using just additional text data, avoiding the need for further intensive training of the extensive pre-trained ASR model. This is particularly advantageous for Korean, characterized as a low-resource language, which confronts a significant challenge due to limited resources of speech data and available ASR models. Initially, we validate the efficacy of training the n-gram model at the clause-level by contrasting its inference accuracy with that of the E2E ASR model when merged with language models trained on smaller lexical units. We then demonstrate that our approach achieves enhanced domain adaptation accuracy compared to Shallow Fusion, a previously devised method for merging an external language model with an E2E ASR model without necessitating additional training. [ABSTRACT FROM AUTHOR]

Copyright of Studii de Ştiintă şi Cultură is the property of Studii de Stiinta si Cultura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Studii de Ştiintă şi Cultură is the property of Studii de Stiinta si Cultura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: This study explores the impact of noise-induced hearing loss (NIHL) on the microstructural integrity of white matter tracts in the brain, focusing on areas involved in speech processing. While the primary impact of hearing loss occurs in the inner ear, these changes can extend to the central auditory pathways and have broader effects on brain function. Our research aimed to uncover the neural mechanisms underlying hearing loss-related deficits in speech perception and cognition among NIHL patients. Methods: The study included two groups: nine bilateral NIHL patients and nine individuals with normal hearing. Advanced diffusion tensor imaging techniques were employed to assess changes in the white matter tracts. Regions of interest (ROIs), including the auditory cortex, cingulum, arcuate fasciculus, and longitudinal fasciculus, were examined. Fractional anisotropy (FA) values from these ROIs were extracted for analysis. Results: Our findings indicated significant reductions in FA values in NIHL patients, particularly in the left cingulum, right cingulum, and left inferior longitudinal fasciculus. Notably, no significant changes were observed in the auditory cortex, arcuate fasciculus, superior longitudinal fasciculus, middle longitudinal fasciculus, and right inferior longitudinal fasciculus, suggesting differential impacts of NIHL on various white matter tracts. Conclusions: The study's findings highlight the importance of considering association fibres related to speech processing in treating NIHL, as the broader neural network beyond primary auditory structures is significantly impacted. This research contributes to understanding the neurological impact of NIHL and underscores the need for comprehensive approaches in addressing this condition. [ABSTRACT FROM AUTHOR]

Copyright of Dental Press Journal of Orthodontics is the property of Dental Press International and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study was to evaluate the postoperative course and long-term functional and aesthetic outcomes in patients with Beckwith-Wiedemann syndrome (BWS) following surgical reduction of macroglossia, using multiple questionnaires. Patients with BWS who underwent keyhole reduction for macroglossia were included in this study. The postoperative course for each patient was recorded, and multiple questionnaires were administered to evaluate aesthetic concerns, oral incompetence or feeding difficulties, sleep-disordered breathing symptoms, and speech. Nine patients underwent ten reduction glossoplasty surgeries. The mean age at surgery was 22 months. The postoperative course for each case was uneventful, except for one patient who had wound dehiscence. The questionnaires revealed significant improvements in tongue appearance, feeding, drooling, facial appearance, and psychosocial outcomes. There was also a significant reduction in sleep-disordered breathing symptoms after surgery. Keyhole reduction glossoplasty is a safe and effective procedure for the treatment of macroglossia in BWS patients, with excellent functional and aesthetic outcomes and a low complication rate. [ABSTRACT FROM AUTHOR]

Speech improvement is an indispensable technology in the field of speech interaction. Speech enhancement uses a variety of techniques and algorithms to increase the quality and intelligibility of speech. Background noise is always present when there is a speech signal. To retrieve the intended speech signal from the damaged speech signal, speech systems must use excellent noise reduction algorithms. This article presents an application-oriented approach for separating and enhancing preferred speech signals using circular microphone array for audio conferencing systems. The source separation for conferencing (SSC)method calculates the number of active speech signals and the direction of arrival by processing the arrival angle, phase variations, and the time difference in arrival. The direction of the signal is determined using Time Difference of Arrival (TDOA) technique. The adaptive least mean square (LMS) algorithm and the transformed TDOA signal can be enhanced to obtain the preferred signal from the selected location. The SSC method yields an SNR of 7.8 dB, SDR of 6.9 dB, SIR of 8.3 dB, and PESQ of 2.2 as the signal quality metrics of an input signal to the enhanced desired signal. The efficiency of the proposedmodel is compared with the existingmodels like PL-CNN, DL-MVDR, and SUDoRMRF. [ABSTRACT FROM AUTHOR]

The abundance of openly available audio data in English enables pretraining of automatic speech recognition (ASR) systems on hundreds of thousands to millions of hours of recorded speech. As a result speech recognition systems are approaching human level robustness in English. Other languages' performance in multilingual speech recognition systems tend to stand in proportion to the amount of data included from the language – or the language family – in question. For low to mid resource languages with fewer speakers, the amount of openly available data may be limited, and as a consequence these languages tend to be underrepresented in large scale efforts to train multilingual speech recognition systems. The National Library of Sweden hosts large collections of audio recordings. These resources allow us to potentially bridge some of the existing speech recognition performance gaps between Swedish and higher resource languages. We believe Swedish speech recognition can be further improved upon by scaling up the amount of training data. We have constructed an inclusive and transparent speech corpus with emphasis on all variations of spoken Swedish that we will use to train speech-to-text models for Swedish. [ABSTRACT FROM AUTHOR]

The article examines Vilém Flusser's views on feminism and his relationships with women, particularly his wife Edith. While their marriage appeared traditional, Edith's contributions were crucial to Flusser's work. Flusser engaged in discussions about women's liberation and the redefinition of love, acknowledging the importance of feminism but also expressing concerns about its impact on traditional notions of love. Some critics found Flusser's treatment of women problematic, while others emphasized his charm and intellectual relationships with women. Flusser's views on feminism and love were paradoxical, as he recognized the importance of feminism but also questioned its compatibility with traditional love. [Extracted from the article]

The current study investigates the influence of Age, Lexical frequency, and Language background on children's overregularization of irregular past participles (e.g., ponido 'put', cf. puesto). A total of 536 past participles used in compound tenses (e.g., he escrito 'I have written') were extracted from child speech in online corpora from both monolingual Spanish (N = 45) and Spanish-English bilingual children (N = 2) between the ages of 3;0 and 6;11. The children overregularized 7 % of their past participles and did so more often with low-frequency participles like escrito 'written' than with high-frequency ones like hecho 'done/made'. Additionally, Spanish-English bilinguals overregularized more than the monolinguals, and the bilinguals' overregularizations persisted with age while the monolingual children's rates decreased with age. These results demonstrate that both monolingual and bilingual children overregularize irregular past participles in the structures of interest and that their overregularizations are mediated by lexical frequency. Further, these results suggest that children must be exposed to enough examples of irregular forms before they stop applying the regular pattern. While the monolingual children who were exposed to extended amounts of Spanish retreated from participle overregularization with age, bilinguals' rates of overregularization persisted through the ages studied. These findings indicate that children from both speaker groups follow a similar acquisitional path of irregular past participles; however, language experience predicts their rates of overregularization, and the timespan required to master the irregulars. [ABSTRACT FROM AUTHOR]

The capacity to speak in public requires personal skills that not everyone has. Anxiety and nervousness, many times easily classified as phobia, can represent serious handicaps and committing the expected speaking performance. Being a subject rather explored by contemporaneous literature, this research wants to contribute to exploring Virtual Reality and Gamification strategies for mitigating those difficulties. During this project a VR application for Google Cardboards was developed, to allow speakers to immersively face simulated 3D scenarios. With the aim of representing real-life scenarios as closely as possible, after each speaking session, the assessment process allows the user to find his limitations and reorient future experiences. Speakers are assessed on four indicators, namely voice tone, audience visual contact, head movements, and speech duration. Scenarios and speeches can be created dynamically by specialists, and classified into different difficulty levels, allowing each participant to define his own learning path. Experimentations were done with two distinct groups of students and the main results outcomes are described and analyzed. This research constitutes part of an ERASMUS+ funding project. [ABSTRACT FROM AUTHOR]

This paper introduces a sign language translation system designed to bridge the communication gap between Hard of Hearing (HOH) or mute individuals, and address challenges in job opportunities and mental health. The system offers bi-directional translation of sign language and speech/text. For sign language to speech/text translation, the system utilizes either camera-based gestures, or data-glove inputs. The reverse translation is facilitated through a 3D avatar demonstrating the corresponding sign language for the input speech or text. Our findings demonstrate the system's translation accuracy and its potential to empower HOH and mute individuals in various communication scenarios. [ABSTRACT FROM AUTHOR]

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INTRODUCTION: Early cognitive decline may manifest in subtle differences in speech. METHODS: We examined 238 cognitively unimpaired adults from the Framingham Heart Study (32–75 years) who completed amyloid and tau PET imaging. Speech patterns during delayed recall of a story memory task were quantified via five speech markers, and their associations with global amyloid status and regional tau signal were examined. RESULTS: Total utterance time, number of between‐utterance pauses, speech rate, and percentage of unique words significantly correlated with delayed recall score although the shared variance was low (2%–15%). Delayed recall score was not significantly different between β‐amyoid‐positive (Aβ+) and ‐negative (Aβ−) groups and was not associated with regional tau signal. However, longer and more between‐utterance pauses, and slower speech rate were associated with increased tau signal across medial temporal and early neocortical regions. DISCUSSION: Subtle speech changes during memory recall may reflect cognitive impairment associated with early Alzheimer's disease pathology. Highlights: Speech during delayed memory recall relates to tau PET signal across adulthood.Delayed memory recall score was not associated with tau PET signal.Speech shows greater sensitivity to detecting subtle cognitive changes associated with early tau accumulation.Our cohort spans adulthood, while most PET imaging studies focus on older adults. [ABSTRACT FROM AUTHOR]

For individuals with significant disabilities and complex communication needs, verbal speech alone may not enable them to effectively communicate with those around them nor to access literacy. Core vocabulary provides an alternative mode of communication for students to actively participate in literacy activities, such as shared reading, at home and school. In this article, we describe a case in which a parent of a child with significant disabilities and complex communication needs learned core vocabulary and led shared reading with her daughter at home. Results indicated that not only did the child become more interactive during shared reading but that the child showed increased understanding of the purpose and meaning of core vocabulary. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in technology, deep learning, and pose detection. One significant application of these advancements pertains to the real-time detection of sign language from video sources. The motivation behind this research stems from the pressing societal need to enhance the quality of life for individuals with speech impairments. Given the current prominence of online meetings, exacerbated by the COVID-19 pandemic, there is a growing need for systems that can provide individuals with speech impairments greater independence in communication, eliminating the requirement for a human translator. This research proposal advocates for a solution that leverages PoseNet algorithms for the extraction of key pose points, which are subsequently employed within LSTM models for the predictive modeling of sign language gestures. This research paper aims to make several notable contributions to the field of assistive technology and human-computer interaction. The achieved accuracy stands at an impressive 98%, underscoring the robustness and precision of our proposed system. [ABSTRACT FROM AUTHOR]

This study focuses on gender recognition from speech, and the gender classification performances of various machine learning methods such as support vector machines (SVM), k-nearest neighbor (KNN), convolutional neural network (CNN), and random forest (RF) are investigated. In the study, the Turkish subsection of the Common Voice dataset are used. From this data set, 3000 speech data are randomly selected with a homogeneous distribution according to age and gender, and 75% of the data is used in training the models and the remaining 25% is used in testing. The first 40 Mel Frequency Cepstral Coefficients (MFCC) obtained from each speech are averaged on the time axis and then applied as input to the classifiers. In the study, after parameter optimization is performed on the training data set for all models with the grid search method, performance evaluations are made on the test set using the optimum parameters. Experimental results show that in gender classification from speech, CNN is the most prominent classifier with 98.67% accuracy, followed by SVM with 97.33%, kNN with 96.8% and RF with 95.73%. [ABSTRACT FROM AUTHOR]

This research takes a unique approach to improve the accuracy of Parkinson's disease prediction through speech signal analysis by turning to the natural world for inspiration. This research proposes an optimization algorithm modelled on the hunting strategies of eagles. This approach begins with the extraction of Mel-frequency cepstral coefficients (MFCCs) from the audio signals. Subsequently, the proposed algorithm, inspired by the keen eyesight and hunting strategy of eagles, is employed to select the most relevant MFCC features. This optimization ensures a reduced dimensionality while maintaining the integrity and relevance of the feature set. Eagle's highly effective three-pronged approach to hunting - consisting of soaring (exploring), scanning (assessing), and swooping (taking action) - is the starting point for this method. To put this into practice, the proposed algorithm treats each potential combination of audio signal features as a 'solution', or 'prey'. The usefulness or 'fitness' of each solution is evaluated according to how accurately it can predict Parkinson's disease using a Support Vector Machine (SVM) classifier on a validation dataset. At the start, our algorithm generates an array of solutions. It then takes an 'eagle's eye view', assessing and refining these solutions in successive iterations. This mimics the eagle's hunt, where it identifies its target from a distance, before swooping in to capture it. By testing the proposed eagle-inspired algorithm on the Parkinson's Disease Voice Initiative (PDVI) dataset, it was found that it offers better accuracy in differentiating between those with Parkinson's and those without, compared to traditional methods. Specifically, using the proposed algorithm's selected features, the SVM model achieved an accuracy of 93.2% compared to an 88.5% accuracy when using all MFCC features. Furthermore, the computation time was reduced by 15% due to the dimensionality reduction introduced by the proposed algorithm. In conclusion, this study opens up a promising new direction in the early detection and treatment of Parkinson's disease, demonstrating the value of looking to nature for solving complex problems. [ABSTRACT FROM AUTHOR]

This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference in gender identification accuracy among speakers from different geographical areas. Indian speakers, utilizing 52 Hindi and 26 English phonemes in their speech, demonstrate a notably higher gender identification accuracy of 85.75% compared to those speakers who predominantly use 26 English phonemes in their conversations when the system is trained using speech samples from Indian speakers. The gender identification accuracy of the proposed model reaches 83.20% when the system is trained using speech samples from speakers outside of India. In the analysis of speech signals, Mel Frequency Cepstral Coefficients (MFCCs) serve as relevant features for the speech data. The deep learning classification algorithm utilized in this research is based on a Bidirectional Long Short-Term Memory (BiLSTM) architecture within a Recurrent Neural Network (RNN) model. [ABSTRACT FROM AUTHOR]

Many primates produce copulation calls, but we have surprisingly little data on what human sex sounds like. I present 34 h of audio recordings from 2239 authentic sexual episodes shared online. These include partnered sex or masturbation, but each recording has only one main vocalizer (1950 female, 289 male). Both acoustic features and arousal ratings from an online perceptual experiment with 109 listeners recruited on Prolific follow an inverted-U curve, revealing the likely time of orgasm. Sexual vocalizations become longer, louder, more high-pitched, voiced, and unpredictable at orgasm in both men and women. Men are not less vocal overall in this sample, but women start moaning at an earlier stage; speech or even minimally verbalized exclamations are uncommon. While excessive vocalizing sounds inauthentic to listeners, vocal bursts at peak arousal are ubiquitous and less verbalized than in the build-up phase, suggesting limited volitional control. Human sexual vocalizations likely include both consciously controlled and spontaneous moans of pleasure, which are perhaps best understood as sounds of liking rather than signals specific to copulation. [ABSTRACT FROM AUTHOR]

During the remote 2024 Spring Business Meeting of the Visual Resources Association, the VRA president highlighted the accomplishments and challenges of the Association in a State of the Association address. This article provides the transcript. [ABSTRACT FROM AUTHOR]

The number of young native speakers of Taiwanese, the variant of Southern Min spoken in Taiwan, has decreased. Technological advancements such as text-to-speech (TTS) systems could help arrest this decline. The aim of this study was to design a robust tonal phoneme corpus and a speech synthesis system for Modern Literal Taiwanese (MLT). MLT subsyllables were analyzed using phonetics and phonology to establish tonal phoneme models. These robust tonal phoneme models and hidden Markov models were used to construct an MLT TTS synthesis system. Algorithm-based training resulted in 869 balanced sentences containing 12,544 syllables, with each sentence containing an average of 14.4 syllables. In total, 218 sentences, which included rare phonemes, were manually drafted to supplement the corpus. The synthesized phonemes were deemed to have high intelligibility and could be included in the developed TTS system. According to the HTK speech recognition tool, the overall phoneme recognition rate was 96.47%. Testers, who were native Taiwanese speakers, assigned the synthesized sentences a mean opinion score of 4, indicating that they sounded natural. This developed system and the results described herein can inspire future developments in speech technology and computational linguistics. [ABSTRACT FROM AUTHOR]

Speech in children with cleft palate can be affected by velopharyngeal dysfunction, which persists after primary palate repair. The incidence of surgery to correct velopharyngeal dysfunction in this patient group has previously been reported as 2.6–37%. We aimed to investigate the incidence of velopharyngeal dysfunction surgery in Swedish children with cleft palate and to examine potential associations of independent variables with this incidence. In this cohort study, we analysed data from the Swedish cleft lip and palate quality registry for 1093 children with cleft palate with or without cleft lip. Kaplan–Meier analysis was used to estimate the risk of having velopharyngeal dysfunction surgery. Multivariable Cox proportional hazards models were used to estimate the associated effect of cleft subtype, additional diagnoses, gender, and age at and number of stages for primary palate repair on the primary outcome. The risk of having velopharyngeal dysfunction surgery was 25.6%. Complete primary palate repair after the age of 18 months or in more than one stage was associated with a higher risk, but it could not be determined which of these was the more significant factor. Cleft soft palate was associated with a significantly lower risk than other cleft subtypes. Primary palate repair at a higher age or in more than one stage may increase the risk of having velopharyngeal dysfunction surgery. Further analysis of potential unknown confounding factors and the association between the incidence of velopharyngeal dysfunction and surgery to correct this condition is needed. [ABSTRACT FROM AUTHOR]