Speech Recordings Research Articles

Construction of an English Oral Pronunciation Evaluation Model Based on Deep Learning Algorithms

Oral pronunciation refers to the way words are spoken aloud, encompassing aspects like accent, intonation, and clarity. It plays a crucial role in effective communication, influencing listeners to understand the speaker. The purpose of this research is to construct an English oral pronunciation evaluation model based on a deep learning algorithm. In this research, we propose a novel Dung Beetle Optimization-driven Customized Deep Belief Network (DBO-CDBN) for accurate detection and evaluation of English oral pronunciation. We obtain a dataset that comprises audio recordings of English speech, including both correct and mispronounced samples to train and evaluate the suggested pronunciation evaluation model. Data normalization is used to pre-process the gathered raw audio data, it enhances the overall quality of the data. The Gammatone Frequency Cepstral Coefficients (GFCC) algorithm is employed to extract the crucial features from the obtained audio data. In our proposed model, the DBO optimization algorithm iteratively finetunes the CDBN architecture to enhance the evaluation accuracy. The approach, which is executed in python, is shown to be more successful in pronunciation evaluation and to provide prospective advances in the area when its performance is examined and compared to other approaches in terms of precision (96.78%), recall (93.65%), and [Formula: see text]-measure (94.53%). The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across various parameters. In addition, we also performed a comparative analysis using diverse existing methodologies. The findings demonstrate the excellence and effectiveness of the suggested future economic forecasting model.

Матеріали до словника традиційних народних промислів (гончарська лексика подільських говірок). Частина 2

The article analyzes lexical material for the Dictionary of Traditional Folk Crafts. The proposed materials reflect the peculiarities and regional differences of the pottery terminology of Podillia. The relevance of the article is due to the fact that many problems of dialect lexicology have not been solved yet, in particular, a national dictionary of dialects and a dictionary of individual regions of Ukrainian dialects have not been created. The nomination of phenomena and concepts of traditional folk culture is poly-informative, and their fixation and preservation remains an urgent task of modern dialectology. The sources of the factual material are recordings of live folk speech made by the author in 12 (twelve) settlements of Podillia in 1998-1999. The purpose of the research is to review the history of Podillia pottery as a folk craft, as well as to present a fragment of a dictionary of pottery terminology, and to introduce a name to denote the realities of pottery into scientific circulation. The research uses general scientific methods: analysis, synthesis and observation, which made it possible to identify and systematize colloquial material; the descriptive method was used in the process of analyzing the vernacular speech. Special linguistic methods of research: questionnaires and interviews contributed to the recording of dialect speech, contextual and interpretive analysis allowed identifying transformations of the semantic scope of lexemes functioning in the live speech of speakers of Podillia dialects, linguo-geographical method made it possible to illustrate the habitat of the recorded names. The result of the research is the introduction of lexical material collected during the expedition into scientific circulation. The work on the dictionary of pottery terminology is planned to be continued, streamlining the previously collected vocabulary. Studying the cultural characteristics of Ukrainians allows not only expanding and developing general knowledge, but also passing on the nation's invaluable experience to future generations. The vocabulary of traditional crafts is a unique basis for enriching the language, a part of cultural and social memory. It is also important to compile dictionaries, as the recording of traditional folk culture concepts is an urgent issue not only for ethnology but also for linguistics. The pottery vocabulary of Podillia dialects is a complex multifaceted system with a clear structure. The study of this vocabulary proves that in terms of area, it shows tendencies towards spatial opposition: some of the lexemes are not found in neighboring dialects (brashne, bohonok, vedrovyi pot, veseltse, etc.). We see the prospects for further research in the continuation of work on the compilation of the Dictionary of Podillia dialects.

Politeness and Power in Moroccan Political Discourse: The Strategic Use of French Honorifics

This study investigates the relationship between politeness and power in Moroccan political discourse, focusing on the strategic use of French honorifics by political figures. Drawing on Brown and Levinson's (1987) politeness theory, it explores how linguistic strategies assert authority and navigate face-threatening acts in high-stakes interactions. Using multimodal discourse analysis, three video recordings of Moroccan political figures, including interviews and speeches, were analyzed to uncover patterns in the use of honorifics across various social and political contexts. The findings reveal that honorifics function as tools for maintaining social hierarchies, where power dynamics are influenced by age, social status, and cultural norms. Additionally, honorifics not only signify deference and respect but also carry nuanced implications that reflect the complexity of political communication in Moroccan society. This research highlights the critical role of linguistic strategies in shaping authority and social relationships in political discourse, offering insights into the interplay between language, identity, and power. Furthermore, it underscores the importance of cultural and contextual factors in understanding the mechanisms of politeness in a multilingual and politically dynamic setting. Keywords: Politeness Theory, French Honorifics, Moroccan Political Discourse, Power Dynamics, Discourse Analysis.

Reliably quantifying the severity of social symptoms in children with autism using ASDSpeech

Several studies have demonstrated that the severity of social communication problems, a core symptom of Autism Spectrum Disorder (ASD), is correlated with specific speech characteristics of ASD individuals. This suggests that it may be possible to develop speech analysis algorithms that can quantify ASD symptom severity from speech recordings in a direct and objective manner. Here we demonstrate the utility of a new open-source AI algorithm, ASDSpeech, which can analyze speech recordings of ASD children and reliably quantify their social communication difficulties across multiple developmental timepoints. The algorithm was trained and tested on the largest ASD speech dataset available to date, which contained 99,193 vocalizations from 197 ASD children recorded in 258 Autism Diagnostic Observation Schedule, Second edition (ADOS-2) assessments. ASDSpeech was trained with acoustic and conversational features extracted from the speech recordings of 136 children, who participated in a single ADOS-2 assessment, and tested with independent recordings of 61 additional children who completed two ADOS-2 assessments, separated by 1–2 years. Estimated total ADOS-2 scores in the test set were significantly correlated with actual scores when examining either the first (r(59) = 0.544, P < 0.0001) or second (r(59) = 0.605, P < 0.0001) assessment. Separate estimation of social communication and restricted and repetitive behavior symptoms revealed that ASDSpeech was particularly accurate at estimating social communication symptoms (i.e., ADOS-2 social affect scores). These results demonstrate the potential utility of ASDSpeech for enhancing basic and clinical ASD research as well as clinical management. We openly share both algorithm and speech feature dataset for use and further development by the community.

PhonemeVec: A Phoneme-Level Contextual Prosody Representation For Speech Synthesis

Recently, fine-grained prosody representations have emerged and attracted growing attention to address the one-to-many problem in text-to-speech (TTS). In this paper, we propose the PhonemeVec, a pre-trained prosody representations with considering the contextual information. To obtain the contextual prosody representations, we improve the data2vec framework according to the characteristics of prosody to extract the PhonemeVec from the low-band mel-spectrogram, and pre-train on a 960 hours Chinese corpus with high quality and diverse pronunciation. PhonemeVec is subsequently integrated into FastSpeech2, supervising the prosody modeling of the text encoder. Experiments conducted on the Blizzard Challenge 2019 dataset show that the integration of PhonemeVec results in the synthesis of more natural speech. Additionally, objective evaluations confirm that the application of PhonemeVec reduces the distortions between the generated speech and original recordings in terms of duration and F0. Audio samples can be found in http://home.ustc.edu.cn/~wsmzzz/PhonemeVec/demo.html.

A cross-language speech model for detection of Parkinson’s disease

Abstract Speech change is a biometric marker for Parkinson’s disease (PD). However, evaluating speech variability across diverse languages is challenging. We aimed to develop a cross-language algorithm differentiating between PD patients and healthy controls using a Taiwanese and Korean speech data set. We recruited 299 healthy controls and 347 patients with PD from Taiwan and Korea. Participants with PD underwent smartphone-based speech recordings during the “on” phase. Each Korean participant performed various speech texts, while the Taiwanese participant read a standardized, fixed-length article. Korean short-speech (≦15 syllables) and long-speech (&gt; 15 syllables) recordings were combined with the Taiwanese speech dataset. The merged dataset was split into a training set (controls vs. early-stage PD) and a validation set (controls vs. advanced-stage PD) to evaluate the model's effectiveness in differentiating PD patients from controls across languages based on speech length. Numerous acoustic and linguistic speech features were extracted and combined with machine learning algorithms to distinguish PD patients from controls. The area under the receiver operating characteristic (AUROC) curve was calculated to assess diagnostic performance. Random forest and AdaBoost classifiers showed an AUROC 0.82 for distinguishing patients with early-stage PD from controls. In the validation cohort, the random forest algorithm maintained this value (0.90) for discriminating advanced-stage PD patients. The model showed superior performance in the combined language cohort (AUROC 0.90) than either the Korean (AUROC 0.87) or Taiwanese (AUROC 0.88) cohorts individually. However, with another merged speech data set of short-speech recordings &lt; 25 characters, the diagnostic performance to identify early-stage PD patients from controls dropped to 0.72 and showed a further limited ability to discriminate advanced-stage patients. Leveraging multifaceted speech features, including both acoustic and linguistic characteristics, could aid in distinguishing PD patients from healthy individuals, even across different languages.

Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer's disease risk-enriched cohorts.

Mild cognitive impairment (MCI) has been recognized as a possible precursor to Alzheimer's disease (AD). Recent research focusing on connected speech has uncovered various features strongly correlated with MCI due to AD and related dementias. Despite these advancements, the impact of early cognitive decline on articulatory precision has not been thoroughly investigated. This study introduced the phoneme log-likelihood ratio (PLLR) to assess the articulatory precision of speakers across different cognitive status levels and compared its effectiveness to existing well-studied acoustic features. The study utilized speech recordings from a picture description task, which included data from 324 cognitively unimpaired participants with low amyloid-β burden (CU, Aβ()), 47 cognitively unimpaired participants with high amyloid-β burden (CU, Aβ()), 69 participants with MCI, and 20 participants with dementia. Nine acoustic features were extracted from the speech recordings, covering three categories: speech fluency, speech pace, and articulatory precision. Welch's -test and Hedge's were adopted to assess their discriminative ability. A reduction in speech fluency and pace was observed among participants in the MCI and dementia groups. The PLLR shows large effect sizes in distinguishing between cognitively unimpaired participants with low Aβ burden and those diagnosed with MCI or dementia. Additionally, the test-retest reliability experiment indicated moderate repeatability of the features under study. The study reveals PLLR as a sensitive indicator capable of detecting subtle articulatory variations across groups, while also providing further support for the association between reduced articulatory precision and early cognitive decline.

Advancing Healthcare: Intelligent Speech Technology for Transcription, Disease Diagnosis, and Interactive Control of Medical Equipment in Smart Hospitals

Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated learning with Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) neural networks to improve IST performance. Leveraging the “Medical Speech, Transcription, and Intent” dataset from Kaggle, comprising a variety of speech recordings and corresponding medical symptom labels, noise reduction was applied using a Wiener filter to improve audio quality. Feature extraction through MLP and sequence classification with GRU highlighted the model’s robustness and capacity for detailed medical understanding. The federated learning framework enabled collaborative model training across multiple hospital sites, preserving patient privacy by avoiding raw data exchange. This distributed approach allowed the model to learn from diverse, real-world data while ensuring compliance with strict data protection standards. Through rigorous five-fold cross-validation, the proposed Fed MLP-GRU model demonstrated an accuracy of 98.6%, with consistently high sensitivity and specificity, highlighting its reliable generalization across multiple test conditions. In real-time applications, the model effectively performed medical transcription, provided symptom-based diagnostic insights, and facilitated hands-free control of healthcare equipment, reducing contamination risks and enhancing workflow efficiency. These findings indicate that IST, powered by federated neural networks, can significantly improve healthcare delivery, accuracy in patient diagnosis, and operational efficiency in clinical settings. This research underscores the transformative potential of federated learning and advanced neural networks for addressing pressing challenges in modern healthcare and setting the stage for future innovations in intelligent medical technology.

Humans can use positive and negative spectrotemporal correlations to detect rising and falling pitch.

To discern speech or appreciate music, the human auditory system detects how pitch increases or decreases over time. However, the algorithms used to detect changes in pitch, or pitch motion, are incompletely understood. Here, using psychophysics, computational modeling, functional neuroimaging, and analysis of recorded speech, we ask if humans can detect pitch motion using computations analogous to those used by the visual system. We adapted stimuli from studies of vision to create novel auditory correlated noise stimuli that elicited robust pitch motion percepts. Crucially, these stimuli are inharmonic and possess no persistent features across frequency or time, but do possess positive or negative local spectrotemporal correlations in intensity. In psychophysical experiments, we found clear evidence that humans can judge pitch direction based only on positive or negative spectrotemporal intensity correlations. The key behavioral result-robust sensitivity to the negative spectrotemporal correlations-is a direct analogue of illusory "reverse-phi" motion in vision, and thus constitutes a new auditory illusion. Our behavioral results and computational modeling led us to hypothesize that human auditory processing may employ pitch direction opponency. fMRI measurements in auditory cortex supported this hypothesis. To link our psychophysical findings to real-world pitch perception, we analyzed recordings of English and Mandarin speech and found that pitch direction was robustly signaled by both positive and negative spectrotemporal correlations, suggesting that sensitivity to both types of correlations confers ecological benefits. Overall, this work reveals how motion detection algorithms sensitive to local correlations are deployed by the central nervous system across disparate modalities (vision and audition) and dimensions (space and frequency).

A Consecutive Interpreting Study of the Keynote Speech on the Opening Ceremony of the Conference on Dialogue of Asian Civilizations

As a real-time bilingual processing activity, consecutive interpreting consists of three stages:source language listening and identification, information memorization and target language production. This paper chooses the consecutive interpreting recording of keynote speech on the opening ceremony of the Conference on Dialogue of Asian Civilizations as the study text. The author manage to analysis the target language text from lexical level, syntactic level and textual level.

Automatic Speech Recognition of Conversational Speech in Individuals With Disordered Speech.

This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode-specific training on ASR accuracy. Recordings of read and conversational speech from 27 individuals with various speech disorders were analyzed using both (a) one speaker-independent ASR system trained and optimized for typical speech and (b) multiple ASR models that were personalized to the speech of the participants with disordered speech. Word error rates were calculated for each speech model, read versus conversational, and subject. Linear mixed-effects models were used to assess the impact of speech mode and disorder severity on ASR accuracy. We investigated nine variables, classified as technical, linguistic, or speech impairment factors, for their potential influence on the performance gap. We found a significant performance gap between read and conversational speech in both personalized and unadapted ASR models. Speech impairment severity notably impacted recognition accuracy in unadapted models for both speech modes and in personalized models for read speech. Linguistic attributes of utterances were the most influential on accuracy, though atypical speech characteristics also played a role. Including conversational speech samples in model training notably improved recognition accuracy. We observed a significant performance gap in ASR accuracy between read and conversational speech for individuals with speech disorders. This gap was largely due to the linguistic complexity and unique characteristics of speech disorders in conversational speech. Training personalized ASR models using conversational speech significantly improved recognition accuracy, demonstrating the importance of domain-specific training and highlighting the need for further research into ASR systems capable of handling disordered conversational speech effectively.

Icelandic regional pronunciation, attitudes and real-time change: Latest developments

Abstract In this paper we present selected results from the first data collection phase of the Regional Pronunciation, Attitudes and Real-Time Change (RePARC) research project together with results from two pilot studies that preceded the project. The article focuses on four regional variants of phonological variables, i.e., so-called hard speech and voiced pronunciation, which are primarily associated with the north-eastern part of Iceland, and so-called hv-pronunciation and monophthongal pronunciation, which are regarded as south-eastern features. Informants from two different age groups and three different regions (northern Iceland, southern Iceland, and a so-called neutral area) did a pronunciation test, listened to and gave their attitudinal responses to recordings of speech exhibiting features different from those of their own speech, and answered a questionnaire on their attitudes towards their own pronunciation variants. The results indicate that both voiced pronunciation and hv-pronunciation are fading out, while monophthongal pronunciation and, in particular, hard speech are maintained in their respective core areas. With regard to attitudes, it appears that participants generally consider speech that contains northern features to be clearer than speech that does not. Similarly, participants appear to find these northern features to be more easily recognisable, and at the same time, these features seem to be more closely linked to speakers’ self-identity than other regional features.

COMPARATIVE ANALYSIS OF TEMPORAL PROSODIC PARAMETERS IN THE SPEECH OF FOREIGN STUDENTS LEARNING THE BULGARIAN LANGUAGE

The work examines the prosodic realization of the Bulgarian word stress in the speech of Chinese Mandarin speakers, with the aim of outlining the parameters that make the comprehensibility of their speech difficult. Instrumental phonetics methods are used and recordings of read speech are analyzed with the speech signal processing program Praat. The research is focused on the duration of the vowels as one of the acoustic ques of the word stress. To minimize the "inconsistency" of the binary division of stressed and unstressed units in connected speech (discussed in the literature), taking into account the fact that the acoustic keys of stress are subject to intonational shaping by tonal accents and boundary tones, we separately compare the duration of stressed vowels, the unstressed non-final and the unstressed final. The results show that the characteristic ratio of stressed and unstressed non-final vowels for Bulgarian language, in which the former outweigh the latter by about one and a half times, is not reached among Chinese speakers. Therefore, it is difficult to determine which vowel/syllable is stressed in their speech. The unstressed final vowels are longer than the stressed ones in both groups, which is an expected result due to the final lengthening before pause.

Recognition of psychoneurological state of children with autism spectrum disorder by features of speech: acoustic and perceptual features

The work is devoted to studying of recognition of psychoneurological state of children aged 5–14 with autism spectrum disorder (ASD), n = 35, and typically developing (TD) children, n = 45, by adults. A perceptual analysis was carried out, in which adult native speakers of the Russian language (auditors), n = 206, took part. Test sequences (audiotests) with words and phrases of ASD and TD children selected from spontaneous speech recordings were created for perceptual research. Auditors’ task was to determine psychoneurological state of children (typical/atypical development) based on auditory perception. A spectrographic analysis of phrases of children was carried out. Phrases of ASD children in comparison with TD children are characterized by lower speech rate, fewer words, great stressed and unstressed vowel duration, great values of pitch in phrase, word, stressed and unstressed vowel.

Unveiling early signs of Parkinson’s disease via a longitudinal analysis of celebrity speech recordings

Numerous studies proposed methods to detect Parkinson’s disease (PD) via speech analysis. However, existing corpora often lack prodromal recordings, have small sample sizes, and lack longitudinal data. Speech samples from celebrities who publicly disclosed their PD diagnosis provide longitudinal data, allowing the creation of a new corpus, ParkCeleb. We collected videos from 40 subjects with PD and 40 controls and analyzed evolving speech features from 10 years before to 20 years after diagnosis. Our longitudinal analysis, focused on 15 subjects with PD and 15 controls, revealed features like pitch variability, pause duration, speech rate, and syllable duration, indicating PD progression. Early dysarthria patterns were detectable in the prodromal phase, with the best classifiers achieving AUCs of 0.72 and 0.75 for data collected ten and five years before diagnosis, respectively, and 0.93 post-diagnosis. This study highlights the potential for early detection methods, aiding treatment response identification and screening in clinical trials.

Evaluating the effects of continuous pitch and speech tempo modifications on perceptual speaker verification performance by familiar and unfamiliar listeners

A study was conducted to evaluate the effects of continuous pitch and speech tempo modifications on perceptual speaker verification performance by familiar and unfamiliar naive listeners. Speech recordings made by twelve male, native-French speakers were organised into three groups of four (two in-set, one out-of-set). Two groups of listeners participated, where one group was familiar with one in-set speaker group, while both groups were unfamiliar with the remaining in- and out-of-set speaker groups. Pitch and speech tempo were continuously modified, such that the first 75% of words spoken were modified with percentages of modification beginning at 100% and decaying linearly to 0%. Pitch modifications began at ± 600 cents, while speech tempo modifications started with word durations scaled 1:2 or 3:2. Participants evaluated a series of “go/no-go” task trials, where they were presented a modified speech recording with a face and tasked to respond as quickly as possible if they judged the stimuli to be continuous. The major findings revealed listeners overcame higher percentages of modification when presented familiar speaker stimuli. Familiar listeners outperformed unfamiliar listeners when evaluating continuously modified speech tempo stimuli, however, this effect was speaker-specific for pitch modified stimuli. Contrasting effects of modification direction were also observed. The findings suggest pitch is more useful to listeners when verifying familiar and unfamiliar voices.

Individual-level cross-linguistic comparisons in Toronto Cantonese and English high vowels

Previous studies of Toronto Heritage Cantonese have suggested the lack of a similar phonological contrast in English as the source of cross-linguistic influence on the phonetic production of the /y/ vs. /u/ contrast in Cantonese. By comparing both the English and Cantonese production of the same individual speakers in spontaneous speech recordings, this study addresses Chang’s (2021) call for more bilingual studies focused on individual-level cross-linguistic interactions. Results show three distinct patterns of cross-linguistic interaction: P1) three distinct vowels, P2) Cantonese /u/ merged with English /u/, and P3) Cantonese /y/ merged with English /u/. While P1 was the most frequently occurring pattern, the occurrence of P3 exclusively among second-generation speakers, among those with lower Cantonese Production Scores, and among those with the lowest Pillai Scores suggest that cross-linguistic phonetic similarity is what drives decreased acoustic distance between /y/ and /u/ rather than direct transfer of phonological categories.

Modern approach in microsurgical elimination of tongue defects using computerized digital planning

Introduction. The treatment of malignant neoplasms of the oral mucosa implies a combined treatment, whose first stage generally involves surgery. However, the most common non-personalized surgical methods are based on resection of the tongue, often affecting more than half of the organ, which can lead to significant functional deficiency and disability of patients.Objective. To demonstrate the possibility of tongue defect reconstruction using preoperative computer 3D planning through clinical cases.Маterials and methods. From 2021 to 2024, four patients with primary cancer of the lateral surface of the tongue were operated on using this methodology at the Maxillofacial Surgery Department of the NMICO FMBA of Russia. All patients underwent hemiglossectomy and preventive cervical lymphadenectomy on the affected side, with simultaneous reconstruction of the tongue using a radial forearm flap. The average age of the patients was 53 years; males outnumbered females by a ratio of 3:1. Patients presented with stages rT1-rT4; histological examination revealed no regional lymph node involvement (N0) in any patient. Preoperative instrumental examinations included: magnetic resonance imaging (MRI) of the soft tissues of the maxillofacial area with contrast enhancement; multislice computed tomography (MSCT) of the maxillofacial area with contrast enhancement; MSCT of the donor area (forearm) with contrast enhancement; Doppler ultrasound of the brachiocephalic trunk vessels and donor area vessels; transnasal endoscopic laryngoscopy with swallowing tests (three-swallow test) with video recording. All patients were surveyed preoperatively and postoperatively using EORT QLQ — H&amp;N35, EORT QLQ — C30, FACT– H&amp;N, MD Anderson, and VHI-10 questionnaires. Patients with abundant hair in the donor area underwent laser hair removal prior to hospitalization. After performing computer simulation of the surgical intervention using Slicer and Blender software, templates were printed on a Elegoo Saturn 2 printer. Assessment of speech and swallowing functions was carried out by a speech therapist specializing in these areas using the Pokrovsky protocol and Vospector-DSI software at both preoperative and postoperative stages. The attending physician performed anthropophotometry and video recording of patient complaints and speech at all stages of care. The postoperative period was without complications; nasogastric tubes were removed on the 12th day; the average hospital stay was 14 days.Results. The flap survival rate was 100% across the entire series of observations. Oncological radicality was achieved at R0 for all patients. The average duration of the surgical intervention was 288 minutes. In three patients, the acoustic parameters of speech were within normal limits (in one patient, this parameter could not be assessed due to systemic speech underdevelopment caused by hearing impairment). The average syllable intelligibility coefficient according to Pokrovsky was 88%. All patients adapted to their usual diet and continued their professional activities. According to the results of the EORT QLQ — H&amp;N35, EORT QLQ — C30, and FACT — H&amp;N questionnaires, patients rated their quality of life as good in one case and excellent in three cases.Conclusions. The developed algorithm for preoperative computer planning is promising. The use of surgical templates allowed for adequate oncological radicality, synchronized the simultaneous work of two surgical teams, reduced the duration of anesthetic assistance (with an average surgical intervention time of 288 minutes), and achieved good functional and aesthetic results. However, this methodology requires further refinement in a larger group of patients.

SUBDIALECTAL DISCOURSE AND PECULIARITIES OF ITS MENTAL CONTINUUM IN THE LENS OF THE MODERN LINGUISTIC PARADIGM

The article is devoted to the study of specific features of subdialectal discourse and its mental continuum, viewed through the prism of modern linguistic theories. The purpose of the research is to study and describe the functioning and structure features, as well as the specifics of the linguarium of subdialectal discourse. The object of the study is the subdialectal discourse, which covers speech practices recorded in the village of Pishchanyi Brid, Novoukrainskyi district, Kirovohrad region. The subject of the study is the mental continuum of the subdialect speakers, which includes knowledge, perceptions and assessments related to various aspects of their lives. The source of the study was the speech recordings of the native speakers, i.e. the textual corpus of the subdialect. The methods used in the study were modelling, hypothetical method, deductive and inductive methods, descriptive method, observation method, taxonomic method, and continuous sampling method. The peculiarity of the study of subdialectal speech in discourse studies lies in the analysis of the composition of usages, the specifics of the composition of the linguarium and its influence on the structure of discourse, expression of thoughts, interaction of speakers and the formation of the ethnographic identity of peasants who speak the subdialect. The discourse approach allows us to better understand the speech process in a specific socio-cultural and geographical setting, as well as the interaction of speakers within the dialect environment. It involves analysing the discourse of the language group in question, identifying specific lexical, grammatical and phonetic features that are characteristic of certain socio-cultural groups. The metalinguistic interpretation of discourse types in the linguistic paradigm presents dialectal discourse as a reflection of real language use, a set of idiolects and an indicator of changes in the language. The household, ceremonial, religious and existential knowledge contained in the dialect discourse is critical to the well-being and cultural integrity of rural communities. In particular, household knowledge encompasses practical skills in housekeeping, agriculture and crafts. Ceremonial and religious knowledge includes traditions, rituals and beliefs that support the spiritual life and cultural identity of the community, and existential knowledge is related to the understanding of life, death and moral values, helping community members navigate difficult life situations. Prospects for the study include a meta-analysis of dialectal discourse, which can be expressed in different ways in individual subdialects, the development of new methods for studying dialectal language in the digital environment, and the integration of interdisciplinary approaches for a deeper understanding of speech practices and cultural characteristics of speakers.

Analysis of the Gaussian assumption of single sound source measured and processed through sound level meter

Machine learning (ML) techniques are constantly growing in acoustics. Such methods exploit large databases to train algorithms and earn complex feature correlations. The primary goal is to obtain inferences and predictions. Thus, ML often exploits statistics, and understanding the data distribution becomes fundamental when analyzing the available data. However, it is well-known that only some common methods can be used with large databases, e.g., normality tests are discouraged. The present work aims to explore the Gaussian assumption underlying a validated clustering technique used in long-term monitoring of sound level meters. The Gaussianity of single sound sources is analyzed through normal tests, normal probability plots, cumulative distribution functions, and high-order moments. The analysis involves a real-world measurement carried out through a sound level meter and speech recordings obtained from a common database used in machine learning. Results show that a single sound source can be deemed Gaussian in sound level meter long-term measurements, leading to important insights concerning the choice of algorithms.