Individual Speech Research Articles

Active Models in Speech Perception: A Critical Review in Phonetics

This critical review examines four prominent active models of speech perception: quantal theory, motor theory, hyper-hypo-articulation theory, and exemplar theory. Each of these models offers distinctive insights into how listeners perceive, interpret, and categorize speech sounds. Quantal theory explores the non-linear relationship between articulation and acoustic output, emphasizing stable and unstable regions in speech production. Motor theory posits that speech perception is grounded in articulatory gestures, suggesting a direct link between production and perception. The hyper-hypo-articulation theory focuses on the speaker’s adaptive articulation, which ranges from hyper- to hypo-articulation depending on the listener's needs and the communicative context. Exemplar theory, rooted in cognitive psychology, highlights the role of memory and experience, with individual speech sounds compared to stored exemplars in memory to aid perception. Despite their contributions, none of these models offers a comprehensive solution to all speech perception challenges. Each theory is suited to specific contexts, speech features, or tasks, making it crucial for researchers to understand their limitations. Furthermore, the review underscores the importance of selecting the appropriate model for particular research or practical applications, given the variability in their functionality. Finally, it calls for future research to delve deeper into passive models of speech perception and suggests that comparative studies between active and passive models could yield valuable insights.

Neural processing of speech sounds at premature and term birth: ERPs and MMR between 32 and 42 weeks of gestation

Prenatal listening experience reportedly modulates how humans process speech at birth, but little is known about how speech perception develops throughout the perinatal period. The present experiment assessed the neural event-related potentials (ERP) and mismatch responses (MMR) to native vowels in 99 neonates born between 32 and 42 weeks of gestation. The vowels elicited reliable ERPs in newborns whose gestational age at time of experiment was at least 36 weeks and 1 day (36 + 1). The ERPs reflected spectral distinctions between vowel onsets from age 36 weeks + 6 days and durational distinctions at vowel offsets from age 37 weeks + 6 days. Starting at age 40 + 4, there was evidence of neural discrimination of vowel length, indexed by a negative MMR response. The present findings extend our understanding of the earliest stages of speech perception development in that they pinpoint the ages at which the cortex reliably responds to the phonetic characteristics of individual speech sounds and discriminates a native phoneme contrast. The age at which the brain reliably differentiates vowel onsets coincides with what is considered term age in many countries (37 weeks + 0 days of gestational age). Future studies should investigate to what extent the perinatal maturation of the cortical responses to speech sounds is modulated by the ambient language.

Speaking for the downtrodden

Abstract Most linguistic studies of political speaking in the field of critical discourse analysis tend to focus on speeches delivered by prominent politicians either in a domestic party-political setting or in the international arena. Less attention has been afforded to speeches by civil rights activists and campaigners for other progressive causes. To fill this gap, the present paper focuses on political speaking occurring outside of the party-political setting. The data comprises 120 American activist speeches from the years 1808–2016. The analysis focuses on the construction of ingroups and outgroups, and whether the use of personal pronouns is affected by the type of audience. The frequency trends bring forth new information about the referential complexity of pronouns within individual speeches.

Advancing ASD identification with neuroimaging: a novel GARL methodology integrating Deep Q-Learning and generative adversarial networks

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects an individual's behavior, speech, and social interaction. Early and accurate diagnosis of ASD is pivotal for successful intervention. The limited availability of large datasets for neuroimaging investigations, however, poses a significant challenge to the timely and precise identification of ASD. To address this problem, we propose a breakthrough approach, GARL, for ASD diagnosis using neuroimaging data. GARL innovatively integrates the power of GANs and Deep Q-Learning to augment limited datasets and enhance diagnostic precision. We utilized the Autistic Brain Imaging Data Exchange (ABIDE) I and II datasets and employed a GAN to expand these datasets, creating a more robust and diversified dataset for analysis. This approach not only captures the underlying sample distribution within ABIDE I and II but also employs deep reinforcement learning for continuous self-improvement, significantly enhancing the capability of the model to generalize and adapt. Our experimental results confirmed that GAN-based data augmentation effectively improved the performance of all prediction models on both datasets, with the combination of InfoGAN and DQN's GARL yielding the most notable improvement.

Blame and obligation: The importance of libertarianism and political orientation in the public assessment of disinformation in the United States

AbstractDisinformation concerns have heightened the importance of regulating content and speech in digital communication environments. Perceived risks have led to widespread public support for stricter control measures, even at the expense of individual speech rights. To better understand these preferences in the US context, we investigate public attitudes regarding blame for and obligation to address digital disinformation by drawing on political ideology, libertarian values, trust in societal actors, and issue salience. A manual content analysis of open‐ended survey responses in combination with an issue salience experiment shows that political orientation and trust in actors primarily drive blame attribution, while libertarianism predominantly informs whose obligation it is to stop the spread. Additionally, enhancing the salience of specific aspects of the issue can influence people's assessments of blame and obligation. Our findings reveal a range of attributions, underlining the need for careful balance in regulatory interventions. Additionally, we expose a gap in previous literature by demonstrating libertarianism's unique role vis‐à‐vis political orientation in the context of regulating content and speech in digital communication environments.

Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network

Speech separation, sometimes known as the “cocktail party problem”, is the process of separating individual speech signals from an audio mixture that includes ambient noises and several speakers. The goal is to extract the target speech in this complicated sound scenario and either make it easier to understand or increase its quality so that it may be used in subsequent processing. Speech separation on overlapping audio data is important for many speech-processing tasks, including natural language processing, automatic speech recognition, and intelligent personal assistants. New speech separation algorithms are often built on a deep neural network (DNN) structure, which seeks to learn the complex relationship between the speech mixture and any specific speech source of interest. DNN-based speech separation algorithms outperform conventional statistics-based methods, although they typically need a lot of processing and/or a larger model size. This study presents a new end-to-end speech separation network called ESC-MASD-Net (effective speaker separation through convolutional multi-view attention and SuDoRM-RF network), which has relatively fewer model parameters compared with the state-of-the-art speech separation architectures. The network is partly inspired by the SuDoRM-RF++ network, which uses multiple time-resolution features with downsampling and resampling for effective speech separation. ESC-MASD-Net incorporates the multi-view attention and residual conformer modules into SuDoRM-RF++. Additionally, the U-Convolutional block in ESC-MASD-Net is refined with a conformer layer. Experiments conducted on the WHAM! dataset show that ESC-MASD-Net outperforms SuDoRM-RF++ significantly in the SI-SDRi metric. Furthermore, the use of the conformer layer has also improved the performance of ESC-MASD-Net.

DIALECTISMS IN NOVEL "MAMY" BY MARIA MATIOS

The article analyzes the accentuation, phonetic, morphological, word-forming and phraseological dialectisms used in the six-act drama "Mamy" by Maria Mathios. Unlike lexical dialectisms, which are mostly presented in page-by-page footnotes with an interpretation of their semantics by the author, the speech units studied in the proposed article mostly function in the text of the work itself and do not require explanation, as they are completely understandable from the context. Instead, phraseological dialectisms are also often explained in footnotes, since their semantics may not be completely clear to the general public. At the same time, we observe the use of phonetic, morphological, accentuation and phraseological dialectisms precisely in the speech of the characters, although lexical ones are also presented in the author's speech. In addition, the researched work presents significant monologues of characters, which contain a number of various speech units from different structural levels of the language. Among the morphological dialectisms, we note speech features primarily in the field of nouns, in particular, the presence of the ending -y in the genitive singular of feminine nouns of the third declension; in verb forms, primarily in the preservation of ancient forms of the past tense, the function of the participle-postfix -sia is mainly in the preposition; in the writer's representation of a number of dialect forms of official parts of speech, as well as exclamations. Accentuational dialectisms are mostly paroxytonated units. Word-forming dialectisms differ from literary language mainly by suffixes, less often by prefixes. Phraseological dialecticisms used in the text of the novel are emotionally saturated, are an important element of the characters' speech, and function mostly with the main component of the verb and verbal semantics. The researched types of dialectisms recorded in the prose text are presented in dialects of the southwestern dialect, primarily Hutsul, Transnistrian, Bukovinian, Transcarpathian, and Boykish. The analyzed dialect units are an important component of the characters' individual speech, logically woven into the linguistic fabric of the artistic text.

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Inconsistent Phonological Disorder: A Case Report

This case report describes a young child with inconsistent phonological disorder (IPD). The child presented with unintelligible speech. She made limited progress on individual speech production goals targeting phonological patterns over 4 years, remaining unintelligible. The child was diagnosed with IPD following an inconsistency assessment and motor speech assessment. She completed 16 weeks of Core Vocabulary intervention to target phonological planning and consistency of word production. The child made significant gains in intelligibility once the Core Vocabulary intervention was initiated. This case report highlights the importance of identifying and providing appropriate intervention for IPD in young children.

Implications of Changes in the Criminal Procedure Law of the ITE Law on Individual Rights in the Indonesian Legal System

The revision of the Electronic Information and Transactions Law (UU ITE) in Indonesia has become a hot topic of debate. Institutions and activists such as the Institute for Criminal Justice Reform (ICJR) and the Press Legal Aid Institute (LBH Pers) have presented various arguments regarding the impact of these changes. This article outlines the five main arguments advanced by the ICJR and LBH Pers. First, there is an objection to Article 27 paragraph (3) which regulates insults and defamation. Second, changes in criminal procedural law raise concerns about the discretionary authority of law enforcement officials without involving the courts. Third, the addition of cyberbullying-related crimes raises concerns about the potential for over-criminalization. Fourth, governments are given additional powers to filter and block content, which can result in potential abuse. Finally, the fifth argument relates to the "right to be forgotten" clause which can be used as a tool of censorship against news publications and journalists about one's past. This article also examines the impact of information technology developments on computer crime and underscores the importance of protecting individual rights and free speech in the digital age.

Timing and location of speech errors induced by direct cortical stimulation.

Cortical regions supporting speech production are commonly established using neuroimaging techniques in both research and clinical settings. However, for neurosurgical purposes, structural function is routinely mapped peri-operatively using direct electrocortical stimulation. While this method is the gold standard for identification of eloquent cortical regions to preserve in neurosurgical patients, there is lack of specificity of the actual underlying cognitive processes being interrupted. To address this, we propose mapping the temporal dynamics of speech arrest across peri-sylvian cortices by quantifying the latency between stimulation and speech deficits. In doing so, we are able to substantiate hypotheses about distinct region-specific functional roles (e.g. planning versus motor execution). In this retrospective observational study, we analysed 20 patients (12 female; age range 14-43) with refractory epilepsy who underwent continuous extra-operative intracranial EEG monitoring of an automatic speech task during clinical bedside language mapping. Latency to speech arrest was calculated as time from stimulation onset to speech arrest onset, controlling for individual speech rate. Most instances of motor-based arrest (87.5% of 96 instances) were in sensorimotor cortex with mid-range latencies to speech arrest with a distributional peak at 0.47 s. Speech arrest occurred in numerous regions, with relatively short latencies in supramarginal gyrus (0.46 s), superior temporal gyrus (0.51 s) and middle temporal gyrus (0.54 s), followed by relatively long latencies in sensorimotor cortex (0.72 s) and especially long latencies in inferior frontal gyrus (0.95 s). Non-parametric testing for speech arrest revealed that region predicted latency; latencies in supramarginal gyrus and in superior temporal gyrus were shorter than in sensorimotor cortex and in inferior frontal gyrus. Sensorimotor cortex is primarily responsible for motor-based arrest. Latencies to speech arrest in supramarginal gyrus and superior temporal gyrus (and to a lesser extent middle temporal gyrus) align with latencies to motor-based arrest in sensorimotor cortex. This pattern of relatively quick cessation of speech suggests that stimulating these regions interferes with the outgoing motor execution. In contrast, the latencies to speech arrest in inferior frontal gyrus and in ventral regions of sensorimotor cortex were significantly longer than those in temporoparietal regions. Longer latencies in the more frontal areas (including inferior frontal gyrus and ventral areas of precentral gyrus and postcentral gyrus) suggest that stimulating these areas interrupts a higher-level speech production process involved in planning. These results implicate the ventral specialization of sensorimotor cortex (including both precentral and postcentral gyri) for speech planning above and beyond motor execution.

Epigenetic Alterations in Alzheimer's Disease: Impact on Insulin Signaling and Advanced Drug Delivery Systems.

Alzheimer's disease (AD) is a neurodegenerative condition that predominantly affects the hippocampus and the entorhinal complex, leading to memory lapse and cognitive impairment. This can have a negative impact on an individual's behavior, speech, and ability to navigate their surroundings. AD is one of the principal causes of dementia. One of the most accepted theories in AD, the amyloid β (Aβ) hypothesis, assumes that the buildup of the peptide Aβ is the root cause of AD. Impaired insulin signaling in the periphery and central nervous system has been considered to have an effect on the pathophysiology of AD. Further, researchers have shifted their focus to epigenetic mechanisms that are responsible for dysregulating major biochemical pathways and intracellular signaling processes responsible for directly or indirectly causing AD. The prime epigenetic mechanisms encompass DNA methylation, histone modifications, and non-coding RNA, and are majorly responsible for impairing insulin signaling both centrally and peripherally, thus leading to AD. In this review, we provide insights into the major epigenetic mechanisms involved in causing AD, such as DNA methylation and histone deacetylation. We decipher how the mechanisms alter peripheral insulin signaling and brain insulin signaling, leading to AD pathophysiology. In addition, this review also discusses the need for newer drug delivery systems for the targeted delivery of epigenetic drugs and explores targeted drug delivery systems such as nanoparticles, vesicular systems, networks, and other nano formulations in AD. Further, this review also sheds light on the future approaches used for epigenetic drug delivery.

Текстемный анализ специальных текстов по виноделию

This article carries out a comprehensive and multifactorial semantic-structural analysis of special texts on winemaking, taking into account the contact and distant semantic connections of individual speech units (propositional locutemes) among themselves and the relationships of individual sentences, the totality of which makes up the text. The purpose of the study is to develop a typology of prototypical semantic structures (textemes) of special winemaking texts, which will improve the technology for translating winemaking concepts taking into account the context and their meaning. The scientific novelty of the work lies in the fact that for the first time, with the help of linguocogitological methods, built-up contact and distant connections of logosemic features of syntagmemes as unifying links of block textemes have been identified in order to categorize logosemes in special texts on winemaking in the process of semantization. The obtained results make it possible to describe the specific functioning of the components of texts of various levels in the form of a complex of interconnected elements, i. e., the lingvemic-locutematic mechanism for creating explicit and implicit connections of intercategorical logical-semantic features in special texts on winemaking, representing the process of textemization.

'Communication is difficult': Speech, language and communication needs of people with young onset or rarer forms of non-language led dementia.

People with behavioural variant frontotemporal dementia, Lewy body dementia, posterior cortical atrophy and young onset Alzheimer's disease may experience language and communication difficulties. However, the role of speech and language interventions for people with these non-language led dementias has received little attention. This study aimed to explore the experiences and perspectives of people living with these conditions, and their families, regarding their language and communication difficulties and how speech and language therapy could address these needs. This study employed a qualitative design to explore the experiences of people living with or caring for somebody with behavioural variant frontotemporal dementia, Lewy body dementia, posterior cortical atrophy or young onset Alzheimer's disease, and to understand their opinions about speech and language therapy. Participants were recruited from a support service connected to a dementia clinic to attend one of five focus group meetings. Videorecorded focus groups and interviews were transcribed, and reflexive thematic analysis was used to analyse data from people affected by each type of dementia. A total of 25 participants were recruited to the study, with representation across the different forms of non-language led dementias. The four main themes identified were: (1) communication difficulties as a key difficulty, (2) loss and loneliness, (3) speech and language therapy, and (4) the role of the caregiver. Sixteen subthemes were also identified which highlighted individual issues across disease types. Although all the forms of dementia studied here are not considered to be language-led, people with these conditions and/or their care partners identified speech, language and communication as common challenges. These communication difficulties were reported to have a negative impact on their social participation and mental health and participants felt speech and language interventions could help. There is a need for research exploring speech and language interventions developed for and with people with non-language led dementias and their care partners, to ensure they meet the needs of the people they are designed for. What is already known on the subject People with primary progressive aphasia present with speech, language and communication difficulties, and several speech and language interventions have been developed to meet the needs of this population. However, people with non-language led dementias may also experience speech, language and communication difficulties, and little is known about interventions that may address these difficulties. What this paper adds to existing knowledge People living with or caring for somebody with behavioural variant frontotemporal dementia, Lewy body dementia, posterior cortical atrophy and young onset Alzheimer's disease report experiencing speech, language and communication difficulties that impact on the person with dementia's social participation and mood. Participants in this study also shared their opinions about how speech and language interventions could help, from the earliest stages of the disease. What are the potential or actual clinical implications of this work? Speech and language therapists need to address the individual speech, language and communication needs of people with dementias, even those that are not thought to be language-led. Current speech and language therapy service provision does not meet the needs of people with non-language led dementias and further research is required to develop interventions and services to meet these needs.

Fairness and fluency: the political audibility of ‘newcomers’ in Victorian debating clubs and public meetings, 1870–1910

ABSTRACT The turn of the twentieth century saw a significant change in the accessibility of political debate: various reforms made the Second Chamber open to religious, ethnic and gendered ‘others’. These newcomers would eventually contribute to changes in political practice. Throughout the second half of the nineteenth century, however, their gradual inclusion in political speech in extra-parliamentary spaces was also marked by a process of assimilation and negotiation. Like the elite men who already enjoyed exclusive access to political decision-making, these newcomers had to abide by the ‘rules’ of debate, and therefore needed to learn how to behave and how to speak in a manner that would make them politically audible in a system that was largely underpinned by ideologies and ideals of free debate and rational, individual speech. This article argues that embodied practices of speech had an important role to play in the process of inclusion, assimilation and rebellion of newcomers in political debates and parliamentary politics . Taking the appearance of colonial ‘others’ and women in debating clubs as its point of departure and leaning on sources documenting the ideals and practices of various debating clubs in and beyond Victorian Britain, this article aims to show how the often implicit rules of political debate and behaviour were taught and instilled in the second half of the nineteenth century. Assumptions and normative expectations about what constituted legitimate and effective, or ‘fluent’, political speech worked to exclude numerous speakers, but also provided various historical actors with opportunities to make themselves heard.

Classification of dementia from spoken speech using feature selection and the bag of acoustic words model

<p>Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoustic features using the open-source openSMILE library. Word histogram representations were formed from the characterized speech segments of each speaker, which were used for classifying subjects. The formation of word histograms involved a clustering phase where feature vectors were quantized. It is well-known that partitional clustering involves instability in clustering results due to the selection of starting points, which can cause variability in classification outcomes. This study aimed to address instability by utilizing robust K-spatial-medians clustering, efficient K-means$ ++ $ clustering initialization, and selecting the smallest clustering error from repeated clusterings. Additionally, the study employed feature selection based on the Wilcoxon signed-rank test to achieve computational efficiency in the methods. The results showed that it is possible to achieve a consistent 75% classification accuracy using only twenty-five features, both with the external ADReSS 2020 test data and through leave-one-subject-out cross-validation of the entire dataset. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.</p>

Член Союза благоденствия С. Д. Нечаев в Обществе любителей российской словесности при Московском университете

The terms “literary Decembrism” and “Decembrist literature”, which have become established in literary studies, denote the civil trend in Russian romanticism of the 1810s-1820s. In addition to the Decembrist writers themselves, an important role in this direction is assigned to A.S. Pushkin, who has never been a member of secret societies. As an alternative, we consider the case of the writer S.D. Nechaev. He was a member of the Decembrist secret society Union of Prosperity, but remained in the history of Russian literature as the author of sentimental album poems and moralizing aphorisms. His only poem filled with truly civic pathos was “The Drinking Song of the Greeks” (1823). However, for Nechaev, membership in a secret society was not an empty formality. At the end of October he also became a member of the Society of Lovers of Russian Literature at Moscow University. And already in November, Nechaev spoke to “lovers of Russian literature” with a speech “On the choice of objects in the fine arts in general”. The speaker promoted ideas and aesthetic principles that he did not adhere to in his own work, but which were enshrined in the statutory document of the Union of Prosperity. So he denied his own value of art and saw its purpose in serving the “common good”, strengthening morality and virtue, and educating youth. This article substantiates the proposal to consider the speech of Nechaev not as his individual speech, but as a propaganda action of the Union of Prosperity.

Modificações do feedback auditivo e seus efeitos sobre a voz de indivíduos adultos: uma revisão de escopo

ABSTRACT Introduction The auditory perception of voice and its production involve auditory feedback, kinesthetic cues and the feedforward system that produce different effects for the voice. The Lombard, Sidetone and Pitch-Shift-Reflex effects are the most studied. The mapping of scientific experiments on changes in auditory feedback for voice motor control makes it possible to examine the existing literature on the phenomenon and may contribute to voice training or therapies. Purpose To map experiments and research results with manipulation of auditory feedback for voice motor control in adults. Method Scope review following the Checklist Preferred Reporting Items for Systematic reviews and Meta-Analyses extension (PRISMA-ScR) to answer the question: “What are the investigation methods and main research findings on the manipulation of auditory feedback in voice self-monitoring of adults?”. The search protocol was based on the Population, Concept, and Context (PCC) mnemonic strategy, in which the population is adult individuals, the concept is the manipulation of auditory feedback and the context is on motor voice control. Articles were searched in the databases: BVS/Virtual Health Library, MEDLINE/Medical Literature Analysis and Retrieval System online, COCHRANE, CINAHL/Cumulative Index to Nursing and Allied Health Literature, SCOPUS and WEB OF SCIENCE. Results 60 articles were found, 19 on the Lombard Effect, 25 on the Pitch-shift-reflex effect, 12 on the Sidetone effect and four on the Sidetone/Lombard effect. The studies are in agreement that the insertion of a noise that masks the auditory feedback causes an increase in the individual's speech intensity and that the amplification of the auditory feedback promotes the reduction of the sound pressure level in the voice production. A reflex response to the change in pitch is observed in the auditory feedback, however, with particular characteristics in each study. Conclusion The material and method of the experiments are different, there are no standardizations in the tasks, the samples are varied and often reduced. The methodological diversity makes it difficult to generalize the results. The main findings of research on auditory feedback on voice motor control confirm that in the suppression of auditory feedback, the individual tends to increase the intensity of the voice. In auditory feedback amplification, the individual decreases the intensity and has greater control over the fundamental frequency, and in frequency manipulations, the individual tends to correct the manipulation. The few studies with dysphonic individuals show that they behave differently from non-dysphonic individuals.

Are University Students Willing to Communicate in English Language Courses?

Abstract From a pedagogical-psychological point of view, communication in teaching occurs between a teacher and a student or between students themselves. It contributes to the activation of internal mental processes, not only in participating individuals, but it benefits the entire class as a whole. Moreover, in the teaching of a foreign language, communication is not only the means of communication, but it also becomes the content of the lesson. Students verify their knowledge and improve their speaking skills through the meaningful use of the foreign language. The willingness to communicate construct answers the question of why some people are more inclined to share their thoughts, knowledge, and opinions verbally more than others. In the presented research, I investigated students’ willingness to communicate in English language courses on a sample of university students (n = 350) using a quantitative questionnaire survey. The overall results indicate that students are generally willing to communicate in English language classes, i.e. the overall reported level of willingness to communicate among students was high, and the results of individual speech skills did not indicate significant differences. However, a closer look at individual speaking skills shows that students report a higher willingness to communicate for items that describe receptive speaking skills.

Using Spontaneous Speech Recognition as a Biomarker to Distinguish Dementia Patients

AbstractBackgroundDigital biomarkers such as individual speech recordings, are a cost‐effective tool for assessing cognitive status using linguistic and acoustic content. The purpose of this study is to investigate whether Natural Language Processing (NLP) methods and audio feature extraction can distinguish spontaneous speech outputs in dementia patients from a healthy population.Method240 audio samples and transcripts of clinician‐participant dialogues involving the Cookie‐Theft visual description were acquired from the DementiaBank Pitt corpus (Dementia group (DG) n = 169; healthy controls (CG) n = 71). Lexical variety, syntactic complexity, salience of information, disfluency patterns, and sentiment analysis were evaluated. Sound synthesis and an acoustic R analysis package were used to filter and examine acoustic properties of recorded audio such as silence and speech segmentation, pitch tracking, and frequency modulation of vibrato or jitter. DG and CG were compared using non‐parametric Kruskal‐Wallis tests, Dunn’s multiple comparison, and Mann‐Whitney U‐test in the statistical analysis. In comparison to CG, we hypothesized that DG would differ in levels of lexical diversity, grammatical complexity, and overall salience.ResultOverall, text analysis showed no variations in disfluencies between groups, but there were significant differences in lexical diversity, syntactic complexity and reference rate to reality (p<0.0001). Additionally, there were no appreciable differences in the frequency of salient information between the two groups, however the frequency of non‐salient information and disfluencies was considerably higher in the DG (p<0.05). Analysis of acoustic features revealed that DG had longer average speech duration and speech segment durations compared to CG (p<0.001).. On the other hand, the rate of change of acoustic characteristics including pitch, mean entropy, and harmonics‐to‐noise ratios did not significantly differ between DG and CG.ConclusionIn conclusion, this paradigm reveals that, in comparison to healthy controls, dementia patients exhibited increased lexical diversity yet lower linguistic complexity, and a shift towards characterizing non‐salient objects in given stimuli, as well as longer segments of speech. Further examination of the rhythmic and acoustic characteristics of speech may provide a comprehensive framework towards defining speech as a potential digital biomarker for dementia risk, and provide applications to regions experiencing increasing socioeconomic disparities.