Мовознавчі та дидактичні аспекти виходу з української фонетичної кризи

In chapter 6 we described the nature of the speech signal and how its image is sensed and presented to the cerebrum. We noted that because no two vocal tracts are exactly alike, your pronunciation will differ subtly but certainly from my pronunciation. To express these subtle, phonetic differences, linguists invented the International Phonetic Alphabet (IPA). In the fifteenth century, the Great English Vowel Shift caused the writing system of English to deviate from continental European systems, so IPA looks more like French or Italian spelling than English. Thus, when you say beet, we might write it in IPA as [bit], and if I pronounce my [i] a little further forward in my mouth, we could capture this detail of my pronunciation in IPA as [bi+t]. The sounds of the letters of IPA correspond to phones, and the brackets tell us we are attempting to capture pronunciation phonetically, that is, as accurately as possible.

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. This research proposes a platform to improve the user experience of Communications Disorders (CMDS) faculty and their students. Traditionally CMDS faculty have the task of training new scholars in the field on communication disorders in the craft of phonetic transcription utilizing the using the International Phonetic Alphabet (IPA). This can be a phoneme by phoneme transcription of words that have to be analyzed in a phoneme by phoneme process. In the department of Computer Science (CS), we had to opportunity to engage in this participatory design exercise with CMDS to analyze and automate this process. This project utilizes a variant of the Edit Distance algorithm for preprocessing the distance between the phonetic transcriptions of the professor’s provided key and student’s response. We call this platform, the Automated Phonetic Transcription grading tool, APTgt. The platform dramatically speeds up the grading process (i.e. instant feedback) as compared to traditional manual grading. The case discussed is a complex multivariate problem in the field of Communications Disorders of using the International Phonetic Alphabet (IPA) as a transcription language to support students learning the IPA language. Our focus was to develop an engaging, easy to use platform that can support interactive online learning and reduce complexity of evaluation and feedback for transcript tasks.

This thesis examines the issue of ill-formed text (microtext) and shows its impact on sentiment analysis. The source of microtext is Twitter, WhatsApp, Facebook and other social media platforms. Words or phrases not in their standard language format like “lol” (laugh out loud), “c u 2nite” (see you tonight) and “plz” (please) are called out of vocabulary (OOV) terms. On the other hand, words or phrases in their standard forms, for example, “tonight”, “love”, or “talk to you later”, are called In Vocabulary (IV) terms. The motivation behind microtext normalization is its growing presence on both online and offline platforms. Microtext might appear to be an ill-formed text, which requires find-and-replace methods. Traditionally, in a Natural Language Processing (NLP) context, microtexts such as abbreviations and length shortening or extending are normalized, but phonemic and graphemic variation is often ignored. However, the features of microtext can be traced back to social, cognitive and geographic influences. To understand social media text, it is essential to develop a microtext normalization module for traditional and modern NLP models. The first section of this thesis focuses on combining unsupervised learning methods in 2 different chapters, 4 and 5. The first method includes lexicon creation to transform the most frequent microtexts and emoticons on social media and applies them to sentiment analysis. This method shows an accuracy increase of 4% when microtext normalization is applied before sentiment analysis. This method is extended with an application to a pre-trained chatbot to improve the chatbot’s understanding of microtext (social media slangs). This method shows a mean BLEU score of 0.8 for the SMS and Tweet dataset. The second method incorporates a phonetic based approach to transform any microtext into its phonetic equivalent before 1normalizing it. This method shows an accuracy improvement of 6% for sentiment analysis on SenticNet. The second part of the unsupervised learning is in Chapter 5, introducing the IPA-based method for microtext normalization and sentiment analysis. Epitran was used to transform a word to its International Phonetic Alphabet (IPA) equivalent. The International Phonetic Alphabet (IPA) is an alphabetic system of phonetic notation based primarily on the Latin script. There is a significant improvement in microtext normalization task and sentiment analysis over Soundex. The results also show that there is very little redundancy when words are transformed to phonetics in IPA. In addition, the accuracy of polarity detection using this method has an improvement over baseline by 5%. Chapter 6 discusses a simple sequence-to-sequence based Deep Learning method. As the language is dynamic, it is not easy to maintain the lexicon. Deep Learning alleviates the static lexicon with a probabilistic model to transform microtext into its standard form. The findings suggest that a semiotic class of microtext was easy to transform, whereas the phonetic class of microtext was not handled easily. The proposed model improves the pre-trained sentiment analysis model by 4%. We also introduce a corpus for English microtext normalization. The corpus contains microtext-containing sentences along with their correctly spelt sentences and their correct polarity. This dataset can tackle two NLP problems, i.e., microtext normalization and sentiment analysis. This corpus consists of 38% positive, 39.9% negative and 22.1% neutral. A pre-trained model shows an accuracy of 46.4 % on OOV text for sentiment analysis, showing considerable room for improvement. In summary, this thesis introduces several approaches to microtext normalization analysis and focuses on sentiment analysis application. Microtext cannot be thought of as a one-dimensional problem to NLP. It encapsulates many factors like regional (geographical), ethnic (national and racial), and social (class, age, gender, socioeconomic status and education), which cannot be ignored given the growing importance of social media in our daily life.

Megrelian is an unwritten Kartvelian language subdivided into two dialects Zugdidi-Samurzaqano and Senaki-Martvili. Following the approaches widely used in language documentation, the Megrelian texts are described and analyzed by means of FieldWorks Language Explorer (FLEx). The FLeX provides interlinear glossing of data. At this moment the Megrelian database includes 49 analyzed texts (38510 tokens, 26610 unique forms, 3480 sentences). Dialogues and monologues are recorded in both dialect areas and include information about the beliefs, traditions, rituals etc. All texts are represented with free translations in Georgian and English. The analysis includes the following: Wordbase - division into words done automatically; Wordipa - representation of Megrelian text in IPA done by the online converter (Gersamia, Lobzhanidze 2022) developed to convert Megrelian based on Georgian script into the International Phonetic Alphabet (IPA), the ISO 9984 romanization scheme, the Gamkrelidze-Machavariani transliteration system (1965) and vice versa; Morphemes - morphological segmentation of words is done manually. The word is subdivided into root and affixes representing fixed linguistic data; The information on morphemes is given in Lex. Entries, the morphological glossing - in Lex. Gloss, while Lex. Gram. Info describes PoS-es and their appropriate categories. Glossing is done following the Leipzig Glossing Rules and Eurotyp Guidelines; Wordcat - information on PoS-es is given separately as well. The morphosyntactic annotation of Megrelian texts collected during the fieldwork and annotated by means of FLEx will be used as a base for the grammar and online dictionary.

Phonetics and phonology encompass segmental and suprasegmental information. Segments consist of vowels and consonants that are central to conveying the meanings of words. Suprasegmentals are speech attributes that can accompany singular segments and extend over syllables, words, or phrases. Suprasegmental features include features such as tone, intonation, and stress. Chapter 2 introduces segmental features and Chapter 3 focuses on the suprasegmental features of spoken Mandarin Chinese. A brief background on phonetics and phonology is provided in Section 2.1. Readers who are already comfortable with concepts such as consonant, vowel, phoneme, and allophone may wish to skip to Section 2.2. In this book, hànyŭ pīnyīn 汉语拼音 (Pinyin), the official romanization system for Standard Mandarin Chinese, will be used to annotate Chinese sounds. Additionally, IPA (International Phonetic Alphabet), which is devised by the International Phonetic Association as a standardized representation of the sounds of spoken language, will be used occasionally to clarify the accurate pronunciations, since conventional written forms are often unsatisfactory guides of pronunciations.2 Throughout the book, phonetic symbols written in IPA will be given in square brackets to distinguish from the Pinyin system (see Table 2.1 for an example of Chinese word 糖 “candy”). For the sake of simplicity, we use a broad transcription in most cases and keep diacritics to a minimum.

Introduction A phoneme is an abstract unit of sound (‘the smallest distinctive unit of the language’) which can be heard with a variety of pronunciations but which can be understood despite these variations. There are 36 phonemes in French (46 in standard English), represented by the signs of the International Phonetic Alphabet (IPA). A good dictionary will give the pronunciation of words using this alphabet, which is to be found in square brackets after the word concerned (e.g. phoneme [fᴐnem]). There are three types of sound in French: vowels ( voyelles ), consonants ( consonnes ) and glides, also called semi-consonants ( semi-consonnes ) or semi-vowels ( semi-voyelles ). Phonetic transcriptions are conventionally written in square brackets. In the tables below, the sounds to be learnt are on the left, followed by illustrative examples and their full transcriptions in the IPA. Vowels There are 16 vowels, divided into two categories: oral and nasal. Oral vowels [i] pet i t [p∂ti]; mais [mais] [y] voul u [vuly]; ( il a ) eu [y] [u] v ou s [vu]; g ou t [gu] [e] ete [ete]; ch e z [∫e]; l es [|e] [e] l ai t [|e], b e lle [be|]; m ai s [me] [o] p eu [po]; gaz eu x [gazo]; n eu tre [notr]; b œu fs [bo]; j eu ne [3on] [œ] b eu rre [bœr], p eu r [pœr]; b œu f [bœf]; j eu ne [3œn] [∂] d e mi [d∂mi]; j e [3∂] [o] eau [o]; r o se [roz]; m o t [mo]; s au le [so|]; ( le ) notre [notr] [ᴐ] p o mme [pᴐm]; b o rd [bᴐr]; v o gue [vᴐg]; s o l [Sᴐ|]; n o tre [nᴐtr] [a] l a [|a]; la [|a]; p a tte [pat]; ch a t [∫a]; v oi e [vwa] [α] âtre [αtr], pâte [pαt], phrase [frαz], bas [bα]

This study presents a clinically applicable method for visualizing the vocal tract of Korean dysarthria patients using an enhanced Vocal Tract Lab (VTL). Our pipeline transcribes speech with Faster Whisper, converts it to the International Phonetic Alphabet (IPA) and then to the Speech Assessment Methods Phonetic Alphabet (SAMPA), which drives a modified VTL rendering Korean-specific vocal tract shapes. We implemented five new vowels, seven diph-thongs, six consonants, and six modified consonants to sup-port underrepresented Korean phonemes. Expert evaluations confirm its utility for speech therapy and pronunciation as-sessment. While limited in modeling plosive timing and view angles, the approach provides a lightweight, interpretable, and extensible system for clinical and research applications.

The purpose of this study is to analyze and validate the role and effectiveness of language labs and task-based learning in the study of English Language. The effectiveness of language labs in learning a new language has been established in several studies mentioned in this paper. The scope of this paper is to investigate the effectiveness on a cohort of Indian students pursuing Bachelor of Technology in a State Private College, University of Petroleum and Energy Studies. The students attend classes once a week for two hours on language learning components based on listening and speaking. For the same cohort of 89 students, there are five evaluations, conducted every alternate week. The evaluations included three spoken or oral evaluation at the beginning of the course, middle and then end. A transcription test based on International Phonetic Alphabet and listening test based on IELTS level 5 were also a part of the evaluation basket. The same set of students taught English via lectures in classroom during the same period for which there were four evaluations. Two sets of data thus obtained, for the same sample. Appropriate statistical test was conducted to analyze the progress students made in a span of six months while taking both courses. The study compares the effectiveness of teaching English language through two delivery mechanisms- language lab and lecture.

Lexical Similarity (LS) between two languages uncovers many interesting linguistic insights such as phylogenetic relationship, mutual intelligibility, common etymology, and loan words. There are various methods through which LS is evaluated. This paper presents a method of Phonetic Edit Distance (PED) that uses a soft comparison of letters using the articulatory features associated with their International Phonetic Alphabet (IPA) transcription. In particular, the comparison between the articulatory features of two letters taken from words belonging to different languages is used to compute the cost of replacement in the inner loop of edit distance computation. As an example, PED gives edit distance of 0.82 between German word &#x2018;vater&#x2019; ([fa:t&#x0259;r]) and Persian word &#x2018; <fig position="float" orientation="portrait"> <graphic position="float" orientation="portrait" xlink:href="khan6-3137905.eps"/> </fig>&#x2019; ([ped&#x00E6;r]), meaning &#x2018;father,&#x2019; and, similarly, PED of 0.93 between Hebrew word &#x2018; <fig position="float" orientation="portrait"> <graphic position="float" orientation="portrait" xlink:href="khan7-3137905.eps"/> </fig>&#x2019; ([&#x0283;&#x0259;&#x026D;am]) and Arabic word &#x2018; <fig position="float" orientation="portrait"> <graphic position="float" orientation="portrait" xlink:href="khan8-3137905.eps"/> </fig>&#x2019; ([s&#x0259;&#x026D;a:m], meaning &#x2018;peace,&#x2019; whereas classical edit distances would be 4 and 2, respectively. We report the results of systematic experiments conducted on six languages: Arabic, Hindi, Marathi, Persian, Sanskrit, and Urdu. Universal Dependencies (UD) corpora were used to restrict the comparison to lists of words belonging to the same part of speech. The LS based on the average PED between pair of words was then computed for each pair of languages, unveiling similarities otherwise masked by the adoption of different alphabets, grammars, and pronunciations rules.

Panini was the father of language planning. Language policy is recognized as 'language planning'. Scholars of the world's representative languages came together to play a groundbreaking role in revealing the nuances and relationships of various languages. International Phonetic Alphabet (IPA) is a Perimeter in this case. Scholars of the world's representative languages created this history at the first international conference held in Hawaii, America in 1964. After that, language planning began in various ways in the country and abroad. Bengali linguists were no exception. Phonetician Muhammad Abdul Hai presented promising formulas and opinions on various aspects of Bengali language planning. Due to his mysterious death, many plans did not see the light of day. Muhammad Abdul Hai was recognized as a modern linguist in the country and abroad. He unraveled the mysteries of the Bengali language in the style of Panini. Rabindranath Tagore introduced the descriptive trend in the discussion of the Bengali language. Muhammad Abdul Hai followed the trend of the elder two great scholars. The language plan he had for the Bangla department of Dhaka University is still worth following for the Bengali-speaking region.The present article discusses Muhammad Abdul Hai's contribution in this regard

Children need to know their alphabet in order to become proficient in using a dictionary. The focus of this first activity is to learn the alphabet using the international spelling code (otherwise known as the international phonetic alphabet).

This book aims to give an overview of patterns observed in the development of phonology. Children acquiring a variety of languages show amazing similarities, and interesting differences, in their phonological development. This books aims to describe such typical patterns, on the basis of existing studies, and to give an explanation for the curious parallels across the globe on the basis of a consideration of various phonological and developmental theories. The book consists of eight chapters: (1) ‘‘Universal Patterns’’; (2) ‘‘Strategies’’; (3) ‘‘Linguistic Models’’; (4) ‘‘The Earliest Stages’’; (5) ‘‘Non-Linguistic Perspectives’’; (6) ‘‘Towards Production’’; (7)’’ Patterns within Patterns’’; and (8) ‘‘Concluding Remarks’’. Before the first chapter, a chart of consonant and vowel symbols as used in the International Phonetic Alphabet appears. There are two appendices: the first gives the sources for all example words used in the book, and the second gives a number of informal definitions of some of the linguistic terms that are used. The first chapter, ‘‘Universal Patterns’’, presents a number of phenomena that are commonly found in child language, or rather child phonology. Among these are the tendency to reduplicate (i.e. to double) a syllable, e.g. [nene] for French nez, ‘‘nose’’, [dada] for English daddy—the fact that reduplication is widespread in child phonology is nicely illustrated by presenting similar examples from Jordanian Arabic, Mandarin Chinese (for Chinese, unfortunately, the tones are omitted, just as stress marks are mostly omitted from examples in stress languages), German, Japanese, Maltese, Russian, Swedish, Zuni and Catalan. The original sources for the examples are given in an appendix (appendix 1) to avoid too many references; in some cases, data from different sources are combined in one example. This brings the slight

In the past, phonetic transcriptions were made using a wide variety of fonts and formats, which hampered the development of phonetic text processing tools. Today, however, the increasing number of language documentation projects making their data freely available over the Web, combined with the adoption of the Unicode Standard by linguists as “best practice” character encoding, present linguistic software developers with an unprecedented opportunity to develop powerful tools for the analysis of phonetic text. This paper describes the generation of a finite state transducer that converts text represented in the International Phonetic Alphabet into phonetic feature sets.KeywordsSelectional ConstraintInput CharacterPhonetic FeaturePhonetic TranscriptionCode PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter contains a newly crafted English translation and International Phonetic Alphabet (IPA) transcription of a poem by twentieth-century poet, author, and pioneering lexicographer, P. de Villiers Pienaar. “The Evening-Flower,” was set to music by composer Arthur Ellis and is scored for high voice. The more popular setting of a similarly titled song by composer P. J. de Villiers to the poem by Boerneef, “Evening-flower Is a White Flower,” in the song cycle “Seven Ditsy Songs,” should not be confused with Ellis’s setting of “The Evening-Flower.” Illuminating footnotes are provided to enhance the reader’s comprehension of the unique biodiversity of the Southern African landscape and multicultural distinctiveness of its people. Topics include love, longing, nature, and dancing.

The transliterations of all Chinese terms and proper names in this book follow the Chinese phonetic system, generally known to the world as the pinyin system. Basically identifiable with their counterparts in the International Phonetic Alphabet, its consonants are easily pronounceable with the exception of a few which were endowed by their inventor(s) with sound values different from the ones they usually represent in the English language. Readers of this book are advised to familiarize themselves with these sounds so that they are not impeded by them in pronouncing Chinese words throughout this book.