Pronunciation, spelling, registers, punctuation

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.

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.

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 study investigates phonological and lexical impairments in the speech of Igbo/English bilingual aphasics, with a focus on how aphasia affects the realization of phonemes and the use of lexical items across the two languages. Aphasia is an acquired language disorder resulting from brain damage, most commonly caused by stroke, and it may impair language production and comprehension at various linguistic levels. Given Nigeria’s multilingual context and the widespread use of English alongside indigenous languages such as Igbo, bilingual aphasia presents a complex linguistic phenomenon that requires systematic investigation. The study adopts a descriptive research design and is grounded in the theoretical frameworks of structuralism and socio-communicative theory. Data were collected from three Igbo/English bilingual aphasic participants receiving medical care at the University of Port Harcourt Teaching Hospital. Speech samples were elicited through spontaneous conversation, question-and-answer sessions, and object/picture naming tasks. Phonetic transcription using the International Phonetic Alphabet (IPA) was employed for detailed phonological analysis, while lexical performance was assessed through word retrieval tasks in both languages. Findings reveal marked phonological impairments characterized by sound substitutions, deletions, and distortions in both Igbo and English. Notably, phonemes that are structurally complex or absent in one language were more vulnerable to impairment. Lexical analysis shows significant word-finding difficulties in both languages, with greater impairment observed in English (L2) than in Igbo (L1). The aphasics demonstrated slower lexical retrieval, use of inappropriate lexical items, and circumlocution, especially in L2. Overall, the study establishes that while both languages are affected by aphasia, the first language exhibits relative resilience. These findings have important implications for bilingual aphasia assessment and rehabilitation in multilingual contexts.

In this paper, we introduce a speech corpus in Indian languages namely Bengali and Odia, which provides phonetic and prosodic information. Phonetics and prosody are vital parameters in human speech perception, hence systematically studying them will help in performing various speech processing tasks. Motivated by this, we have developed Phonetic and Prosodically Rich Transcribed (PPRT) Speech corpus in Bengali and Oriya languages. In this speech corpus ten hours of read speech, five hours of conversation speech and five hours of extempore speech have been collected. The database has been transcribed using International Phonetic Alphabet (IPA) for representing all possible phoneme variations. Along with the phonetic transcription, prosodic information such as duration patterns of syllables, intonation patterns of phrases and break patterns within and across phrases are represented.

This exploratory study investigated the development of speaking and writing skills of L1 Arabic EFL learners based on their level of perception and understanding of phonetic transcriptions through visualisation of letter-to-symbol representations using the International Phonetic Alphabet (henceforth IPA).The participants were 169 University-level Preparatory Year Program (PYP) male Saudi EFL students.The study was carried out as a pedagogical approach to improve university first year students' pronunciation, correct speech and writing skills.The students selected attended 6, 50-minute Integrated Pronunciation Teaching (IPT) lessons which included IPA transcription codes using both audio and visual teaching methods in addition to one ICT aided lesson.Throughout those lessons, students were initially introduced to the IPA phonetic codes in gradual increase of difficulty and were encouraged to use the monolingual (English-English), Longman Dictionary of Contemporary English (LDCE).Two written tests and one oral test were conducted using a number of carefully selected IPA transcription codes related questions and results were analysed and interpreted.Results obtained showed slight variations between higher and lower ability students in understanding the IPA transcription codes.As a whole, however, the results indicated that students reached a high level of understanding of letter-to-symbol representationsthe IPA system -and oral test results proved that phonological awareness can help Saudi students at tertiary level education improve their writing and speaking skills.Above all, learning the phonetic transcription codes helped them develop a sense of autonomy and competence when using monolingual dictionaries.The study concluded with a brief discussion of the ramifications of the study and the potential for further research.

Development and availability of spoken language corpora in regional languages is of utmost importance for a multicultural and multilingual country like India. The issues of regional bias, accent, unique style and diversity associated with each geographical region and language will have a significant effect on the performance of speech recognition/synthesis systems. In this paper, collection of speech data in Kannada language for prosodically guided phonetic search engine and the issues involved in transcription are explained. The speech corpus consists of data in three different contexts namely, read mode, conversation mode and extempore mode. A four layered transcription namely, phonetic transcription using IPA symbols, syllabification, pitch marking and break marking is done for the entire data. A baseline recognition system for Kannada language is built using HTK for the data collected in different modes and the results are presented.

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.

Turkic languages have been written, and are written, in a variety of scripts and according to various orthographical conventions. Employing all of these script systems or representing them by transliterations would be an inadequate basis for crosslinguistic comparisons in a survey such as the present one. Linguistic data are therefore normally given in a broad phonetic transcription (without brackets) that reflects typical sound segments, whether phonemic or subphonemic, e.g. Turkish ačïḳ 'open', Tatar yün 'cheap', Turkmen soŋ 'end', Uzbek åt 'horse'. Graphic forms representing official orthographies as well as cited transcriptions are given in angle brackets (chevrons), e.g. Turkish ‹açık› 'open', Tatar ‹юнь› 'cheap', Turkmen ‹soň› 'end', Uzbek ‹ot› 'horse'. Standard Turkish examples are, as a rule, rendered in their relatively well-known orthographic shapes, which are easily analyzable in phonetic terms, e.g. ‹açık› 'open'. Where precise phonetic details are needed, IPA (International Phonetic Alphabet) transcriptions are given in square brackets, e.g. Turkish [aʧɯk] 'open'. Transcriptions indicating phonologically relevant units are enclosed in slashes, e.g. Turkish/at/ 'horse'.

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.

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).