Speech Features Research Articles

Forced to Choose Silence: Social Gaming with Disabilities

Background Socialization during gaming is an important aspect of gaming for people with and without disabilities. However, we know little about how gamers with disabilities play with others. Aim This study investigated the social gaming experiences of individuals with disabilities focusing on research questions 1) Who do people with disabilities play with?, and 2) How do people with disabilities communicate while gaming? Method We conducted a survey study which focused on experiences of gaming with disabilities. In total 92 responses were analyzed. Results The results suggest that gamers with disabilities play digital multiplayer games locally and online with friends, family, strangers, and people they know. The most used communication methods were speech, text, and built-in features. However, some were not communicating at all due to a lack of suitable communication methods. Furthermore, communication varied depending on if the gaming was happening online or locally, and depending on the gaming companions. Discussion The results align with previous research indicating that people with disabilities engage in gaming with friends, family, and strangers similar to any other gamer. However, there is a lack of studies on how people generally communicate in games, nonetheless, as speech and text communication methods are most commonly seen in games, we can assume that they are the most used communication methods for all gamers. Limitations and Future Research This study has limitations both related to the survey questions and the survey participants. We cannot be sure if all understood the questions in the same way, furthermore, most of the participants were experienced Finnish gamers with physical disabilities which could have skewed the results. Further research is necessary to address these limitations and broaden the scope of the study's findings. Conclusions Based on the results, we argue that games should provide multiple means of communication to make social gaming more accessible.

Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis

This article presents a transfer-learning-based method to improve the synthesized speech quality of the low-resource Dungan language. This improvement is accomplished by fine-tuning a pre-trained Mandarin acoustic model to a Dungan language acoustic model using a limited Dungan corpus within the Tacotron2+WaveRNN framework. Our method begins with developing a transformer-based Dungan text analyzer capable of generating unit sequences with embedded prosodic information from Dungan sentences. These unit sequences, along with the speech features, provide <unit sequence with prosodic labels, Mel spectrograms> pairs as the input of Tacotron2 to train the acoustic model. Concurrently, we pre-trained a Tacotron2-based Mandarin acoustic model using a large-scale Mandarin corpus. The model is then fine-tuned with a small-scale Dungan speech corpus to derive a Dungan acoustic model that autonomously learns the alignment and mapping of the units to the spectrograms. The resulting spectrograms are converted into waveforms via the WaveRNN vocoder, facilitating the synthesis of high-quality Mandarin or Dungan speech. Both subjective and objective experiments suggest that the proposed transfer learning-based Dungan speech synthesis achieves superior scores compared to models trained only with the Dungan corpus and other methods. Consequently, our method offers a strategy to achieve speech synthesis for low-resource languages by adding prosodic information and leveraging a similar, high-resource language corpus through transfer learning.

Speech Feature Extraction in Broadcast Hosting Based on Fluctuating Equation Inversion

Speech Feature Extraction in Broadcast Hosting Based on Fluctuating Equation Inversion

Exploring Spectrogram-Based Audio Classification for Parkinson's Disease: A Study on Speech Classification and Qualitative Reliability Verification.

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

Tindak Tutur Ekspresif Keluhan dalam Drama Korea “My Liberation Notes”

This article discusses the complaint speech acts found in the dialogues of “My Liberation Notes” drama. This study aimed to identify strategies and linguistic features of each complaint speech act found in the drama. The collected data were analyzed with qualitative descriptive method using Kim Kyung-Ryung’s theory by classifying data in six complaint speech act strategies that appeared in the dialogues. Each strategy and sub-strategy had different linguistic features such as a variety of the words, grammars, sentence types, expressions, etc. 암시하기(amsihagi)/hints strategy used question form sentence and expression of carefulness, 질책하기 (jilchaekhagi)/rebuking used expression of surprise, 비난하기 (binanhagi)/criticism used form of negation, 짜증나기 (jjajeungnagi)/annoyance used interjection and word with annoying meaning, 내적수식어 (naejeoksusikeo)/internal modification used expression and word that highlight politeness, and 외적수식어 (wejeoksusikeo)/external modification appeared with usage of word and expression that weaken and strengthen the meaning of the action.

PROPER NAME AS AN EMOTIVE LEXICAL UNIT UNDER BILINGUALISM IN A MULTIETHNIC LINGUISTIC ENVIRONMENT IN THE LIGHT OF LANGUAGE POSITIVITY THEORY

The article deals with the linguistic category of emotivity. Informal forms of proper names are very re-levant onomastic material for the interpretation and substantiation of the theory of language positivity, since the word-building formants involved in their making, even outside the context and consituation, are able to convey the fi ne nuances of positive human emotions. The purpose of this research is to prove that anthroponymic emotives are both means of explication and implication, manifestation and concealment of emotions. To achieve the goal, a sur-vey of respondents was conducted. The questions used a sample of the most popular, rare and average frequency names in Bashkortostan for 2022, including female names like Amina, Octavia and Valeria, and male names such as Amin, Justin and Vladimir. A number of questions asked about the proper names of informants and their attitudes towards the use of diminutive forms. The analysis of responses showed that most respondents perceived these forms positively. The main scope of these names was limited to communication with loved ones. The frequency of use of informal vocatives in 57.1% of responses was determined by the situation. Participants in the survey off ered 24–80 variants for diminutive forms, which according to respondents implied a subjective positive evaluation. Among the diminutive suffi xes of anthroponyms, the most positive emotional connotation is carried by suffi xes such as -ochkand -ik. Linguistic and cultural analysis has allowed us to identify value dominants and stereotypes of communica-tive behavior among linguistic personalities. The extralinguistic interpretation of diminutive proper names provides a reason to regard onomastic units (anthroponyms) as positively connotated emotives. Based on the results of the research, we can conclude that the ethnically specifi c feature of speech in bilingual conditions, using the onomastics of the Republic of Bashkortostan as an example, is the tradition of person naming that has developed in the native language as well as the use of Russian word-formation patterns.

Speaker-listener neural coupling correlates with semantic and acoustic features of naturalistic speech.

Recent research has extensively reported the phenomenon of inter-brain neural coupling between speakers and listeners during speech communication. Yet, the specific speech processes underlying this neural coupling remain elusive. To bridge this gap, this study estimated the correlation between the temporal dynamics of speaker-listener neural coupling with speech features, utilizing two inter-brain datasets accounting for different noise levels and listener's language experiences (native vs. non-native). We first derived time-varying speaker-listener neural coupling, extracted acoustic feature (envelope) and semantic features (entropy and surprisal) from speech, and then explored their correlational relationship. Our findings reveal that in clear conditions, speaker-listener neural coupling correlates with semantic features. However, as noise increases, this correlation is only significant for native listeners. For non-native listeners, neural coupling correlates predominantly with acoustic feature rather than semantic features. These results revealed how speaker-listener neural coupling is associated with the acoustic and semantic features under various scenarios, enriching our understanding of the inter-brain neural mechanisms during natural speech communication. We therefore advocate for more attention on the dynamic nature of speaker-listener neural coupling and its modeling with multilevel speech features.

Instrument recognition in unprocessed musical audio through transformer-based modeling

This study focuses on identifying primary instruments in musical audio using an adapted Wav2Vec 2.0 model, initially intended for extracting speech features from raw audio. Modifications to the model's convolutional layers and transformer element were made to facilitate the recognition of instruments in complex audio mixes. The task of instrument recognition is approached as a multi-labelled classification problem. The effectiveness of the model is measured through accuracy, precision, recall, F1-score, and analysis via a confusion matrix. Key findings reveal the model's differential efficiency in recognising various instruments, with notable success in detecting violins, pianos, saxophones, and human voices. However, the model encounters difficulties in recognising instruments with a narrower dynamic range or lower volume, like the organ that may provide harmonic support, and those with scarce representation, such as the cello and clarinet. The research also indicates that while pre-separation of certain instruments like guitars may enhance recognition, it may not be necessary for others.

Feature-Enhanced Multi-Task Learning for Speech Emotion Recognition Using Decision Trees and LSTM

Speech emotion recognition (SER) plays an important role in human-computer interaction (HCI) technology and has a wide range of application scenarios in medical medicine, psychotherapy, and other applications. In recent years, with the development of deep learning, many researchers have combined feature extraction technology with deep learning technology to extract more discriminative emotional information. However, a single speech emotion classification task makes it difficult to effectively utilize feature information, resulting in feature redundancy. Therefore, this paper uses speech feature enhancement (SFE) as an auxiliary task to provide additional information for the SER task. This paper combines Long Short-Term Memory Networks (LSTM) with soft decision trees and proposes a multi-task learning framework based on a decision tree structure. Specifically, it trains the LSTM network by computing the distances of features at different leaf nodes in the soft decision tree, thereby achieving enhanced speech feature representation. The results show that the algorithm achieves 85.6% accuracy on the EMO-DB dataset and 81.3% accuracy on the CASIA dataset. This represents an improvement of 11.8% over the baseline on the EMO-DB dataset and 14.9% on the CASIA dataset, proving the effectiveness of the method. Additionally, we conducted cross-database experiments, real-time performance analysis, and noise environment analysis to validate the robustness and practicality of our method. The additional analyses further demonstrate that our approach performs reliably across different databases, maintains real-time processing capabilities, and is robust to noisy environments.

Assessing the Language Abilities of Preterm-Born Children: An Examination of Standardized Testing and Language Sample Analysis.

To understand how best to assess the language abilities of preterm-born children, this study: (a) compared preterm- and term-born children's language skills using standardized testing and language sample analysis (LSA), (b) investigated how executive function skills and the language sampling context respectively affect standardized test and LSA scores, and (c) examined the pattern of associations between standardized test and LSA scores among preterm-/term-born groups. Twenty-five term-born and 23 preterm-born 2-year-old singletons were administered the language scales of the Bayley Scales of Infant and Toddler Development-Third Edition (receptive communication, expressive communication, language composite scores). Parent-child free-play recordings were used to quantify the (para)linguistic features of the children's speech. Executive function was measured via parent report. The preterm-born group obtained significantly lower scores than the term-born group on all Bayley language measures (though differences were not consistently observed when using cutoff scores). Few preterm-term differences in LSA measures were found. The preterm-term differences in Bayley scores were not explained by between-group differences in executive function. Some preterm-term differences in LSA scores were moderated by the language sampling context. The preterm- and term-born groups exhibited different patterns of Bayley-LSA correlations. Preterm language difficulties were more apparent on standardized test than LSA scores. Nonetheless, the Bayley-LSA correlations indicate that poor test performance (linked with preterm birth) is associated with functional communication difficulties. The discussion outlines the complementary utility of standardized tests and LSA while acknowledging the limited utility of cutoff scores and the confounding influence of the language sampling context. https://doi.org/10.23641/asha.26142661.

A Stylistic Analysis of Joe Biden's Inaugural Political Speech

In this paper, a stylistic approach has been used to investigate the linguistic features of President Joe Biden's inauguration political speech. Special importance is given to its linguistic characteristics that affect the structure of the selected political speech. This paper is an attempt to highlight the political language of Biden’s political speech. The aim is to seek out how political speeches are packaged to persuade citizens and readers and how stylistic analytical tools assist readers in interpreting the language of Biden in the selected speech. The data has been collected from Time Magazine. The data selection is restricted to Biden's inauguration political speech and has been chosen based on certain foregrounding features that indicate a powerful stylistic effect on readers. The qualitative method has been used to analyze the selected data. In the analysis, some linguistic features, such as graphological, syntactic, grammatical deviations, transitivity, and modality have been used to investigate the style of the selected speech to reveal persuasion. The findings show that Biden's inauguration political speech achieves power, and persuasion through the use of certain stylistic features including lexical and grammatical categories such as abstract nouns, personal pronouns, mental processes, and certain modal auxiliaries. It is concluded that the lexical items that Biden uses are crucial in passing his message to his nation. However, the tendency to use distinctive foregrounding features in the consistent repetition of lexical items and sentence structures are stylistic technique used by Biden for the sake of persuasion.

The role of lexical and prosodic characteristics of mothers' child-directed speech for the early vocabulary development of Italian children with cochlear implants.

Variability in the vocabulary outcomes of children with cochlear implants (CIs) is partially explained by child-directed speech (CDS) characteristics. Yet, relatively little is known about whether and how mothers adapt their lexical and prosodic characteristics to the child's hearing status (before and after implantation, and compared with groups with normal hearing (NH)) and how important they are in affecting vocabulary development in the first 12 months of hearing experience. To investigate whether mothers of children with CIs produce CDS with similar lexical and prosodic characteristics compared with mothers of age-matched children with NH, and whether they modify these characteristics after implantation. In addition, to investigate whether mothers' CDS characteristics predict children's early vocabulary skills before and after implantation. A total of 34 dyads (17 with NH, 17 with children with CIs; ages = 9-32 months), all acquiring Italian, were involved in the study. Mothers' and children's lexical quantity (tokens) and variety (types), mothers' prosodic characteristics (pitch range and variability), and children's vocabulary skills were assessed at two time points, corresponding to before and 1 year post-CI activation for children with CIs. Children's vocabulary skills were assessed using parent reports; lexical and prosodic characteristics were observed in semi-structured mother-child interactions. Results showed that mothers of children with CIs produced speech with similar lexical quantity but lower lexical variety, and with increased pitch range and variability, than mothers of children with NH. Mothers generally increased their lexical quantity and variety and their pitch range between sessions. Children with CIs showed reduced expressive vocabulary and lower lexical quantity and variety than their peers 12 months post-CI activation. Mothers' prosodic characteristics did not explain variance in children's vocabulary skills; their lexical characteristics predicted children's early vocabulary and lexical outcomes, especially in the NH group, but were not related to later language development. Our findings confirm previous studies on other languages and support the idea that the lexical characteristics of mothers' CDS have a positive effect on children's early measures of vocabulary development across hearing groups, whereas prosodic cues play a minor role. Greater input quantity and quality may assist children in the building of basic language model representations, whereas pitch cues may mainly serve attentional and emotional processes. Results emphasize the need for additional longitudinal studies investigating the input received from other figures surrounding the child and its role for children's language development. What is already known on the subject Mothers' CDS is thought to facilitate and support language acquisition in children with various language developmental trajectories, including children with CIs. Because children with CIs are at risk for language delays and have acoustic processing limitations, their mothers may have to produce a lexically simpler but prosodically richer input, compared to mothers of children with NH. Yet, the literature reports mixed findings and no study to our knowledge has concurrently addressed the role of mothers' lexical and prosodic characteristics for children's vocabulary development before implantation and in the first 12 months of hearing experience. What this study adds to the existing knowledge The study shows that mothers of children with CIs produce input of similar quantity but reduced variety, and with heightened pitch characteristics, compared to mothers of children with NH. There was also a general increase in mothers' lexical quantity and variety, and in their pitch range, between sessions. Only their lexical characteristics predicted children's early vocabulary skills. Their lexical variety predicted children's expressive vocabulary and lexical variety only in the NH group. What are the practical and clinical implications of this work? These findings expand our knowledge about the effects of maternal input and may contribute to the improvement of early family-centred intervention programmes for supporting language development in children with CIs.

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.

Detecting Offensive Language on Malay Social Media: A Zero-Shot, Cross-Language Transfer Approach Using Dual-Branch mBERT

Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat—offensive comments. A group of users posts comments that are provocative, discriminatory, and objectionable, intending to disrupt online discussions, provoke others, and incite intergroup conflict. These comments undermine citizens’ legitimate rights, disrupt social order, and may even lead to real-world violent incidents. However, current automatic detection of offensive language primarily focuses on a few high-resource languages, leaving low-resource languages, such as Malay, with insufficient annotated corpora for effective detection. To address this, we propose a zero-shot, cross-language unsupervised offensive language detection (OLD) method using a dual-branch mBERT transfer approach. Firstly, using the multi-language BERT (mBERT) model as the foundational language model, the first network branch automatically extracts features from both source and target domain data. Subsequently, Sinkhorn distance is employed to measure the discrepancy between the source and target language feature representations. By estimating the Sinkhorn distance between the labeled source language (e.g., English) and the unlabeled target language (e.g., Malay) feature representations, the method minimizes the Sinkhorn distance adversarially to provide more stable gradients, thereby extracting effective domain-shared features. Finally, offensive pivot words from the source and target language training sets are identified. These pivot words are then removed from the training data in a second network branch, which employs the same architecture. This process constructs an auxiliary OLD task. By concealing offensive pivot words in the training data, the model reduces overfitting and enhances robustness to the target language. In the end-to-end framework training, the combination of cross-lingual shared features and independent features culminates in unsupervised detection of offensive speech in the target language. The experimental results demonstrate that employing cross-language model transfer learning can achieve unsupervised detection of offensive content in low-resource languages. The number of labeled samples in the source language is positively correlated with transfer performance, and a greater similarity between the source and target languages leads to better transfer effects. The proposed method achieves the best performance in OLD on the Malay dataset, achieving an F1 score of 80.7%. It accurately identifies features of offensive speech, such as sarcasm, mockery, and implicit expressions, and showcases strong generalization and excellent stability across different target languages.

The image of a Japanese as seen by a Dutchman in the English novel and its Ukrainian translation

The article deals with the problem of rendering a literary image of a foreigner in translation. The object of research is the cumulative literary image of a Japanese on the material of David Mitchell’s novel “The Thousand Autumns of Jacob De Zoet” and the subject of research is the means and strategies of its reproduction in the Ukrainian translation. The aim of the research is to conceptualize the above strategies and determine the factors that have an impact on the translator’s decision-making. In particular, the attention is drawn to the factor of ethnic stereotypes that underline the formation of a foreigner’s image in a literary discourse. The article offers a hierarchic view of a literary image as an interaction of three levels of analysis: mega-image (correlated with the idea/theme/conception of a literary piece), macro-image (literary image per se) and micro-image (linguistic and/or stylistic means of a macro-image embodiment). Correspondingly, the translator’s attention should be concentrated on equivalent rendering micro-images, but in a manner consistent with corresponding macro-images and the mega-image of the whole text. Every literary image of a foreigner is based on certain ethno-stereotypes, also known as heterostereotypes that govern the author’s selection of micro-images and consequently the whole structure of a macro-image. Three aspects of a Japanese’s macro-image were singled out: appearance; speech characteristics; traditions, customs and rituals. At the next stage, the micro-images of these aspects’ verbalization were singled out and the translator’s strategies of their re-creation were analyzed. The conclusion was made that due to the similarity in foreigners’ stereotyping by the British and Ukrainians, the translator had no need for cultural adaptations and their strategy was predominantly aimed at overcoming interlinguistic asymmetry.

*Corresponding author: suwandi.sumartias@unpad.ac.id E-ISSN: 2289-1528 https://doi.org/10.17576/JKMJC-2024-4002-01 Portrait of Hate Speech Propagators' Behaviour in Indonesia Twittersphere: A Comparative Virtual Ethnography Study

Political life in Indonesia today cannot be separated from the social media platform Twitter (X) because it is often used to express opinions freely. Unfortunately, a handful of parties misused the freedom of communication and expression offered by Twitter (X) to spread hate speech. One of the biggest phenomena is “Cebong” and “Kadrun”. This research examines the characteristics of hate speech spreaders in the political context of "Cebong" and "Kadrun". This study employed virtual ethnography to interpret the phenomenon of political hate speech on Twitter (X) in May 2022. The hate speech made by "Cebong" and "Kadrun" can be concluded as two hostile political camps. They argued with each other to show their identity as supporters of one of the camps and aimed to bring down the opposing political camp. The tweets made by “Cebong” were dominated by arguments classified as arguments by analogy; meanwhile, "Kadrun" was dominated by discovery arguments: arguments from cause to effect. The manipulation strategy carried out by "Cebong" is dominated by connotations, and "Kadrun" is dominated by specific warrants and connotations. The fallacy expressed by "Cebong" is dominated by persuasive definitions; meanwhile, "Kadrun" is dominated by hasty generalizations. These findings could be suggestions for creating a healthier ecosystem in the digital public sphere in Indonesia. Twitter (X) users should be more aware of reporting content that indicates hate speech. By knowing the characteristics of hate speech and the characteristics of hate speech spreaders, Twitter users can stop the spread of hate speech. Keywords: Cebong, Kadrun, hate speech, propagators, Twitter.

CFNAM-PG: Bridging Phonetic and Glyphic Information for Chinese Full Name and Abbreviation Matching Based on Simbert and DenseNet

Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related to near homophones and near homoglyphs. First, a near-homophone full-abbr matching model based on Simbert and VGG was first proposed, which integrates character and speech features, leveraging a speech recognition model and combining a brain-like cognitive learning dual-process mechanism which involves linguistic knowledge and neural network together. Second, to address the problem of near-homoglyph full-abbr matching in Chinese, a DenseNet-based model that fuses glyph structure and image features was proposed, in which statistical feature extractors are employed to extract feature vectors for glyphic features including stroke, Wubi and structural features separately. Lastly, the near-homophone model and the near-homoglyph model are coupled to work together in the full-abbr matching task, in which expert knowledge is used as a component of the feature optimizer. Experimental results showed that the integrated model significantly increased the matching accuracy to 87.5%, demonstrating a 12.3% improvement.

The Effectiveness of L2 Pronunciation Instruction: A Critical Systematic Review of the Intervention Studies

Pronunciation instruction studies have taken considerable attention in the field of foreign language teaching and research in recent years. In this systematic review, only the intervention studies indexed in SSCI were included. A literature search up to April 2024 was conducted using the Web of Science and relevant meta-analytic studies. 55 interventions met the eligibility criteria based on the PRISMA 2020. This review is twofold: to examine the effects of English L2 pronunciation instruction and to identify the methodological status of these studies in terms of treatment formulation, design, sampling type/size, treatment duration and outcome measures. Results showed that pronunciation instruction treatments positively affected L2 users’ pronunciation performance. Regarding research methodology, the studies employed mostly pre- and post-tests, with at least one experimental group having relatively few delayed tests. The most common participant group was undergraduate students with pre-intermediate and intermediate levels. The participants' performance tended to be measured through technological tools in recent years. The suprasegmental features of speech increasingly took place compared to the mere segmental features. These studies also tended to include native speakers’ ratings in the assessment phase of the instruction. The findings of this study are assumed to provide insights and recommendations for future research studies

Корпусная интерпретация англоязычного экологического дискурса: лингвистические закономерности и функциональные особенности

English-language environmental speech has its own linguistic features. Ecological discourse is a relevant, self-sufficient, and highly identical communication continuum. This article introduces a corpus analysis of the English-language environmental discourse with its linguistic patterns and functional interpretation. The texts were considered not as static linguistic objects, but as a system of functional features in speech. The English-language research material was authentic and featured various environmental issues. Corpus analysis is a popular and reliable tool for big linguistic data while the corpus-discursive methodology proved to be an effective interdisciplinary tool. It was applied to the EuroNews Green Corpus, aggregated from 506 English-language texts on environmental issues. This innovative type of corpus can be qualified as instrumentally independent. The research proved the effectiveness of universal case managers or case shells of open architecture, e.g., Sketch Engine. Not only did it manage to collect the data, but also performed quite a complex structure analysis, interpreting, and discourse modeling. Corpus linguistics is looking for qualitatively new opportunities to identify complex linguistic relations. The case study provided a comprehensive description of environmental discourse as an object of linguistic research. Based on statistic details, the authors analyzed the categories of tokens, keywords, and terms to structure them as lemmas, collocations, and n-grams, which were presented as metadata. The metadata modeling provided concordances, thesauruses, and frequency glossaries. Environmental discourse had a pronounced lexical and semantic uniqueness resulting from specific ecological terminology. However, the multidimensional corpus analysis revealed an extremely low frequency of the key concept of ecology. Thus, this corpus study of the English-language ecological discourse demonstrated a good potential for further linguistic research.

The effect of detoxification on acoustic features of Mandarin speech in male heroin users.

This study aims to investigate the effect of detoxification on acoustic features of Mandarin speech. Speech recordings were collected from 66 male abstinent heroin users with different durations of drug detoxification, specifically early abstinent users with a detoxification duration of less than 2 years, sustained abstinent users with 2 years of detoxification, and long-term abstinent users with a detoxification duration of more than 2 years. The results of the acoustic analyses showed that early abstinent users exhibited lower loudness, relative energies of F1, F2, and F3, higher H1-A3, and fewer loudness peaks per second, as well as a longer average duration of unvoiced segments, compared to the sustained and long-term abstinent users. The findings suggest that detoxification may lead to a rehabilitation process in the speech production of abstinent heroin users (e.g., less vocal hoarseness). This study not only provides valuable insights into the effect of detoxification on speech production but also provides a theoretical basis for the speech rehabilitation and detoxification treatment of heroin users.