Dialect Corpus Research Articles

Spor etter det samiske kasussystemet i indrefinnmarksmålet

The article deals with the Norwegian dialect of the interior of Finnmark county. This is an area thathas historically been closely linked to the Finno-Ugric population. The coexistence of Norwegianas a representative of the Indo-European languages and Sámi as a representative of the Uraliclanguages may have resulted in several specific features. The aim of this article is to identify thosefeatures related to the Sámi case system: occurrence of atypical prepositions and an unusuallocal perspective. In my analysis, I rely on data from the Nordic Dialect Corpus (Nordiskdialektkorpus) – an electronic database that contains records of authentic dialect conversationsin various regions of Norway. The interior of the county of Finnmark is represented by threeplaces: Kautokeino, Tana and Lakselv.

Complementizer drop and clitic climbing in Torlakian: Evidence from the Timok variety

The paper is based on a corpus study of the complementizer da drop (Comp_drop) and clitic climbing (CC) in the Timok variety of Serbian. Data are taken from the Spoken Torlak dialect corpus 1.0 (Vuković 2020) and comprise over 900 examples of sentences containing the following selecting verbs: modal verbs moći ‘can’ and morati ‘must’, light verbs ići ‘go’ and uzeti ‘take’, and the aspectual verb početi ‘begin’, and their complement clauses, with or without the complementizer da. Two predicted variables, Comp_drop and CC, are analyzed alongside eleven predictor variables. Statistical analyses reveal that Comp_drop is optional and significantly affected by the person of the selected verb, the availability of clitics in the structure, and the presence of the impersonal construction. On the other hand, CC is possible but rare. It occurs only when the complementizer is dropped, and is affected by the type of verb, particularly facilitated by the aspectual verb početi ‘begin’. The results are discussed in terms of the structural size of the complement clauses, in line with Author1’s (2022) proposal of a clause size smaller than the smallest one attested in the standard variety and Aljović’s (2005) observation that CC is available from functionally poor domains, i.e. from restructuring complements.

Emotion Recognition in Kurdish Speech from the Sorani Dialect Corpus

Given the increasing need for interactive human-computer applications, the field of employing machine learning algorithms to discern emotions from speech has seen a substantial surge in interest. While emotion recognition systems have made substantial progress in languages like German, English, Spanish, Dutch, and Danish, the availability of comprehensive datasets for the Kurdish language remains notably limited. This paper addresses this gap by focusing on emotion recognition in Sorani Kurdish dialect speech data, which was carefully gathered from openly available videos from the YouTube platform and categorized into four clear supposed emotions: neutral, sadness, happiness, and anger. The study applied both natural Mel Spectrogram and Mel-Frequency Cepstral Coefficient (MFCC) features for various spectrals, followed by the classification models K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) to evaluate the results. By closely examining and contrasting the results of using several methods for feature extraction, it was found that SVM obtained a higher accuracy, reaching as much as 85.57%. This is so much more than the first Kurdish emotion classification technique for the recognition of the emotion of the words.

Lexicon-Based Approach in Sentiment Analysis of Yemeni Dialect for Social Media Network

Recently, the number of Yemeni users has been expanding quickly on social media platforms. Most research in Arabic sentiment analysis has gained on Modern Standard Arabic (MSA) and some specific dialects, such as Egyptian, Levantine, and Gulf. However, there is a noticeable gap in Yemeni dialect sentiment analysis research. The reason for that is the lack of reliable Yemeni lexical and corpus and a real dataset for social media sentiment analysis. This research addresses this lack by presenting the Yemeni Dialect sentiment lexicon and corpus. This lexicon and corpus provide valuable resources for researchers and practitioners seeking to analyze sentiment in Yemeni dialect social media content, contributing to a better understanding of Yemeni public opinion, social media monitoring, marketing, cultural understanding, and assisting in efforts to respond to crises in Yemen. The Yemeni Dialect sentiment lexicon is enriched with a reasonable number of words and phrases categorized according to their positive and negative sentiment tendencies. Moreover, we constructed a corpus dataset of more than 54,000 comments built from the Facebook platform. A large dataset of unlabeled comments from the main Yemeni telecommunications companies in Yemen (Yemen Telecom, Yemen Mobile, YOU, and Sabafon), are people commenting on a public issue related to the services provided by those companies. The lexicon-based approach is used to extract the sentiment’s polarity and label each of the provided comments to formulate a corpus dataset as being either positive, negative, or neutral. The evaluation metrics of experiments are accuracy, recall, precision, f-measure, and the confusion matrix. The accuracy result of the lexicon-based labeling approach was calculated through a comparison between the achieved results and the ones achieved through manually labeled comments by three Yemeni experts. Evaluation results using a lexicon-based approach achieved an accuracy of 90.05%.

Advancing AI-Driven Linguistic Analysis: Developing and Annotating Comprehensive Arabic Dialect Corpora for Gulf Countries and Saudi Arabia

This study harnesses the linguistic diversity of Arabic dialects to create two expansive corpora from X (formerly Twitter). The Gulf Arabic Corpus (GAC-6) includes around 1.7 million tweets from six Gulf countries—Saudi Arabia, UAE, Qatar, Oman, Kuwait, and Bahrain—capturing a wide range of linguistic variations. The Saudi Dialect Corpus (SDC-5) comprises 790,000 tweets, offering in-depth insights into five major regional dialects of Saudi Arabia: Hijazi, Najdi, Southern, Northern, and Eastern, reflecting the complex linguistic landscape of the region. Both corpora are thoroughly annotated with dialect-specific seed words and geolocation data, achieving high levels of accuracy, as indicated by Cohen’s Kappa scores of 0.78 for GAC-6 and 0.90 for SDC-5. The annotation process leverages AI-driven techniques, including machine learning algorithms for automated dialect recognition and feature extraction, to enhance the granularity and precision of the data. These resources significantly contribute to the field of Arabic dialectology and facilitate the development of AI algorithms for linguistic data analysis, enhancing AI system design and efficiency. The data provided by this research are crucial for advancing AI methodologies, supporting diverse applications in the realm of next-generation AI technologies.

Annotation and evaluation of a dialectal Arabic sentiment corpus against benchmark datasets using transformers

Annotation and evaluation of a dialectal Arabic sentiment corpus against benchmark datasets using transformers

An In-depth Corpus-based Investigation of Unbound Reflexives

ABSTRACT This paper provides a comprehensive study of real-life uses of unbound reflexives (URs) in two syntactic structures, coordination and prepositional object structures, using a large amount of synchronic, diachronic, and dialectal corpus data from some Brigham Young University (BYU) corpora. Our findings show that 1) in contemporary American English, URs have different raw frequencies and proportion patterns in the two syntactic structures from anaphoric/cataphoric reflexives and also vary according to their person values; 2) the uses of URs in the two structures have shown decreasing tendencies for the past 200 years in American English; and 3) URs are more frequently used in Irish English than any other inner-circle variety of English in relation to anaphoric reflexives, which is more prominent in coordination structures than in prepositional object structures. Our findings also contribute to providing an empirical foundation for testing existing theories of URs and developing Binding Conditions in general.

Chinese dialect speech recognition: a comprehensive survey

As a multi-ethnic country with a large population, China is endowed with diverse dialects, which brings considerable challenges to speech recognition work. In fact, due to geographical location, population migration, and other factors, the research progress and practical application of Chinese dialect speech recognition are currently at different stages. Therefore, exploring the significant regional heterogeneities in specific recognition approaches and effects, dialect corpus, and other resources is of vital importance for Chinese speech recognition work. Based on this, we first start with the regional classification of dialects and analyze the pivotal acoustic characteristics of dialects, including specific vowels and tones patterns. Secondly, we comprehensively summarize the existing dialect phonetic corpus in China, which is of some assistance in exploring the general construction methods of dialect phonetic corpus. Moreover, we expound on the general process of dialect recognition. Several critical dialect recognition approaches are summarized and introduced in detail, especially the hybrid method of Artificial Neural Network (ANN) combined with the Hidden Markov Model(HMM), as well as the End-to-End (E2E). Thirdly, through the in-depth comparison of their principles, merits, disadvantages, and recognition performance for different dialects, the development trends and challenges in dialect recognition in the future are pointed out. Finally, some application examples of dialect speech recognition are collected and discussed.

Символический компонент семантики номинаций одежды (на материале говоров Среднего Приобья)

The article examines the symbolic component of the semantics of nominations for items of clothing in Russian traditional costume. The symbolic component of the meaning of the analyzed lexical units was identified through linguoculturological analysis. With its help, representatives are identified that testify to it as a cultural sign. The purpose of linguocultural analysis is to determine the totality of connotative components of the meaning of a word, including the symbolic one. To optimally structure a commentary, an answer to several questions is necessary: identifying the degree of objectivity of the procedure, clarifying the scope of its content, as well as determining the sequence of inclusion of components through which the symbolic semantics of a word is represented. The aim of the work is to determine the specifics of objectification of the cultural-symbolic component of the semantics of clothing nominations. The objectives are to identify lexical representative units that have a symbolic component of meaning in their semantics and to determine the set of symbolic components of meaning in clothing names. The research material was data from the dialects of the Middle Ob region recorded in the dictionaries of the Tomsk Dialectological School and the database of the Tomsk Dialect Corpus. The empirical basis of the work was formed using a continuous sampling method. The article implements the general scientific method of description. Methods of interpretation and linguocultural commentary are used. Techniques of contextual analysis, classification, systematization are the main ones when working with the studied layer of lexical units. The illustrative material included in the article contains facts of actualization of the symbolic component of clothing nominations in living dialect speech. Linguoculturological analysis, which allows stating that there is a symbolic connotative component in the semantics of the studied units, shows the idea of the symbolic in culture, which is firmly rooted in the archaic consciousness because it establishes a model of integral existence, knowledge about the orderliness of the world on the principles of correlation between phenomena and processes.

The Role of Students in the Revitalisation of Dialects

Ukrainian society is gradually transitioning from a totalitarian model of „fighting dialects” to recognizing the value of linguistic diversity. This chapter presents examples of the expanding role of dialects and their changing status in contemporary life. The prestige of dialects largely depends on how speakers, particularly young people, perceive them. Today’s students, as future leaders of language policy in Ukraine, will shape the status of dialect speech. From this perspective, engaging students in scientific research and projects aimed at preserving and revitalising dialects is essential, especially through modern multimedia tools, such as the creation of a corpus of Ukrainian dialects – a database showcasing Ukrainian dialect speech in audio and transcribed forms. An all-Ukrainian fund of dialect recordings, collected across universities and stored on modern digital platforms, is also proposed as a significant step forward. Keywords dialects of the Ukrainian language, language policy, oral dialect corpus, dialect revitalisation, social evaluation of language, prestige of dialect speech, dialectological practice.

The Tomsk Dialect Corpus: a comprehensively annotated database of a Siberian Russian dialect from material collected over the last 70 years

The Tomsk Dialect Corpus: a comprehensively annotated database of a Siberian Russian dialect from material collected over the last 70 years

The voice as a material clue: a new forensic Algerian Corpus.

Dialects have received bigger interest in recent years as they are increasingly used on the web and social media. Because Algerian Arabic dialects suffer from a lack of appropriate speech corpora for speech recognition, a rich dialect corpus is needed to approach Algerian Accent recognition. The latter remains a key feature in the field of Forensic Voice Comparison (FVC) systems. This paper presents a new large-scale forensic Algerian speech corpus called Sawt El-Djazaïr. An important criterion in dealing with forensic corpora is the presence of session variability. For this purpose, we collected celebrity recordings in various regions of Algeria, from different social networks, in various scenarios, and at different times. In addition, we also recorded 87 participants using cellular calls and voice over IP (VoIP) applications including Viber, WhatsApp, and Google Meet. The corpus of approximately 50hours covers various speech topics and is spoken in twelve Algerian sub-dialects. The design guidelines of the proposed corpus are described along with the grouping of dialects across different geographical locations. Sawt El-Djazaïr is available to the research community upon request.

Concept “Document” in the Speech of Peasants (Based on the Materials of the Tomsk Dialect Corpus)

Concept “Document” in the Speech of Peasants (Based on the Materials of the Tomsk Dialect Corpus)

Геоинформационный веб-ресурс «Диалектный корпус бурятского языка»

The study aims to provide open access to structured and annotated sound data of the dialect corpus of the Buryat language. It was decided to present the corpus on the Web in the form of a geoinformation system with data binding to a digital map, since the territorial principle plays one of the leading roles in the classification of Buryat dialects. First, the programme of the speech corpus was compiled and sound recordings performed by informants - speakers of the dialects were obtained. The recorded material was segmented and annotated in the ELAN programme. The next step was to develop a programme that allows transferring data from ELAN format files to a relational database. To present data on the Internet, a web application was developed in the form of an interactive digital map based on Google Maps Platform. As a result, a web resource was created that provides users with access to audio dialect data presented in an annotated and structured form and displayed according to the geographic principle. Scientific novelty lies in introducing into scientific and public use materials of a fundamentally new type that make it possible to obtain information about the modern sound of Buryat dialects, as well as to conduct research on modern Buryat dialect speech.

The pragmatics and syntax of pronoun preposing

Preposed pronouns have a dual role of both connecting the utterance to the context and serving as its starting point. While central to understanding restrictions on syntactic movement to the left periphery, this type of fronting has often been overlooked. We provide an analysis of the pragmatics and syntax of Swedish pronoun preposing based on a study of spontaneously produced spoken language from the Nordic Dialect Corpus. We find that there is a strong preference for preposing pronouns that function as switch topics and that it is also possible to prepose continued topics, but in neither case is the preposing obligatory. We argue that these sentence-external relations of the topic are not encoded in the syntax of the left periphery in Swedish, but reflect pragmatic strategies for discourse progression. The types of topic (switch or continued) are furthermore shown not to correlate with the prosodic realization of the pronoun. A key feature of our analysis is that we distinguish the sentence-external function of the preposed phrase from the sentence-internal function of providing the aboutness topic for the utterance. We adopt an analysis of the left periphery proposed by Holmberg, where FinP acts as a bottleneck, and there is only one, syncretic, Force-Topic head above Fin. The fact that we only see evidence for one topic position to the left of the subject in Swedish sets this language apart from German and Italian as they are analyzed by e.g. Frey and Frascarelli and Hinterhölzl, as does the fact that A'-movement of pronouns in Swedish does not imply any particular prosodic marking.

Nasal epenthesis in preverbal accusative clitic pronouns. A variationist study of present-day dialectal European Portuguese

The paper investigates nasal epenthesis in vowel-initial preverbal 3rd person accusative pronouns in modern dialectal European Portuguese (EP). The study is underpinned by the data from the verbatim transcription section of CORDIAL-SIN, a dialectal corpus of contemporary EP. Speakers’ behaviors are analyzed in the fifteen localities where variation is found. Different grammars (prosody-syntax mappings) are singled out, depending on whether the alveolar nasal is extended on preverbal clitic pronouns only or is found in other monosyllabic clitic words (definite articles and demonstrative pronouns) as well. Analogical extensions are demonstrated to be instrumental in inducing speakers to use the nasal-initial allomorph. The analysis points also to the varying realizations of proclisis triggers. Besides surfacing as a nasal diphthong, their last syllable frequently ends in a monophthong, its vocalic nucleus denasalizes or its quality gets altered. Finally, the historical profile of this sandhi process is approached. The change is argued to have spread from grammatically and communicatively unmarked contexts, close to orality. Rather than positing a continuous transmission of nasal epenthesis across generations, emphasis is placed on the consistency with which preverbal clitic pronouns were treated in various periods and in different communicative circumstances.

High dimensional autonomous computing on Arabic language classification

Hyper vectors are holographic and randomly processed with independent and identically distributed tools. A hyper vector includes whole data merged as well as spread completely on its pieces as an encompassing portrayal. So, no spot is more dependable to store any snippet of data compared to others. Hyper vectors are joined with tasks likened to expansion, and changed the structure of numerical processing on vector regions. Hyper vectors are intended to analyze the closeness utilizing a separation metric over the vector region. These activities are nothing but hyper vectors in which it can be joined into intriguing processing conduct with novel highlights which make them vigorous and proficient. This paper focuses on a utilization of hyper dimensional processing for distinguishing the language of text tests for encoding sequential letters into hyper vectors. Perceiving the language of a given book is the initial phase in all sorts of language handling. Examples: text examination, arrangement, and interpretation. High dimension vector models are mainstream in Natural Language Processing and are utilized to catch word significance from word insights. In this research work, the first task is high dimensional computing classification, based on Arabic datasets which contain three datasets such as Arabiya, Khaleej and Akhbarona. High dimensional computing is applied to obtain the results from the previous dataset when it is applied to N-gram encoding. When utilizing SANAD single-label Arabic news articles datasets with 12 N-gram encoding, the accuracy of high computing is 0.9665%. The high dimensional computing with 6 N-gram encoding while utilizing RTA dataset, provides the accuracy of 0.6648%. ANT dataset with 12 N-gram encoding in high dimensional computing gives the accuracy 0.9248%. The second task is applying high dimensional computing on Arabic language recognition for Levantine dialects three dataset is utilized. The first dataset is SDC Shami Dialects Corpus which contains Jordanian, Lebanese, Palestinian and Syrian. The same provides an accuracy of 0.8234% while it is applied to high dimensional computing with 7 N-gram encoding. PADIC (Parallel Arabic dialect corpus) is the second dataset which contains Syria and Palestine Arabic dialects that provide an accuracy of 0.7458% when applied high dimensional computing with 5 N-gram encoding. The high dimensional computing when applied to third dataset MADAR (Multi-Arabic dialect applications and resources) with 6 N-gram encoding provides the accuracy rate of 0.7800%.

Constructing twitter corpus of Iraqi Arabic Dialect (CIAD) for sentiment analysis

The number of Twitter users in Iraq has increased significantly in recent years. Major events, the political situation in the country, had a significant impact on the content of Twitter and affected the tweets of Iraqi users. Creating an Iraqi Arabic Dialect corpus is crucial for sentiment analysis to study such behaviors. Since no such corpus existed, this paper introduces the Corpus of Iraqi Arabic Dialect (CIAD). The corpus has been collected, annotated and made publicly accessible to other researchers for further investigation. Furthermore, the created corpus has been validated using eight different combinations of four feature-selections approaches and two versions of Support Vector Machine (SVM) algorithm. Various performance measures were calculated. The obtained accuracy, 78 %, indicates a promising potential application.

Region-level arabic dialect identification using deep learning models

The automatic classification of the Arabic language dialects is the preliminary step towards establishing many dialectal-sensitive Arabic natural language processing tasks. Arabic dialect identification entails predicting the dialects associated with specific textual inputs and classifying them under their respected labels. In this paper, we propose a novel approach by merging several distinct datasets to obtain a large, diverse, and bias-free dialectal corpus. Our dataset is a collection of parallel sentences translated into multiple dialects (MADAR), as well as tweets gathered from Twitter users (NADI-2020, NADI-2021, QADI, & ARAP-Tweet 2.0). The collected dataset is classified into seven labels: Gulf, Levant, Iraq, Maghreb, Nile Basin, Yemen, and Modern Standard Arabic. The merged dataset was cleansed to produce Arabic sentences free of punctuation, non-Arabic characters, numbers, repeating characters, empty lines, and elongation characters. We obtained a high dialectal classification accuracy using a new Word2vec embedding model on the merged dialectal dataset. Seven deep learning systems were trained on a balanced subset of the dataset. These models are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional-LSTM (BILSTM), Bidirectional-GRU (BIGRU), Convolutional-LSTM (CLSTM), and Convolutional-GRU (CGRU). Single-label classification tests ran on these trained models acquired a minimum accuracy of 77.90% (CNN) and a maximum accuracy of 81.52% (BILSTM). We further evaluated the accuracy of tests ran for short and long sentences with 87.52% attained accuracy for classifying short sentences with CGRU and 94.06% for long sentences using BIGRU, both of which are indications of proposed approach efficacy to move forward.

Жанровая разметка Томского диалектного корпуса: от концепции - к реализации

The relevance of the study is due to the fact that it has been conducted at the intersection of two scientific fields: corpus linguistics and communicative dialectology. The paper presents a comparative analysis of corpus practice based on the material of spoken language. Also, consideration is given to the process and results of creating a discursively annotated corpus of dialect speech with a size of more than 2 million tokens. Discursive markup implies the labeling of three parameters: topic, type, and genre of the text. The novelty of this research project is related to the fact that, for the first time, a large array of dialectal texts has been marked up according to intentional orientation: not only folklore but also speech genres have been annotated. The value of the new source is provided by the combination of archived data with current materials. A methodological advantage of the corpus is the possibility of combining qualitative and quantitative analysis. The paper describes the principles of implementing the genre marking in the Tomsk dialect corpus, with the factors influencing the composition of the genres of the dialect corpus revealed. The results make it possible to determine the quantitative ratio of different speech genres in dialectal communication. The observations are supported by examples from the spoken language of villagers. The accuracy of the conclusions is ensured by the reliance on quantitative data and a considerable amount of material (over 16 thousand genre fragments).