Speech Perception

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 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.

Experiencing Speech: A Skills-Based, Panlingual Approach to Actor Training, Second Edition is a beginner’s guide to Knight-Thompson Speechwork®, a method that focuses on universal and inclusive speech training for actors from all language, racial, cultural, and gender backgrounds and identities. This book provides a progression of playful, practical exercises designed to build a truly universal set of speech skills that any actor can use, such as the ability to identify, discern, and execute every sound found in every language on the planet. By observing different types of flow through the vocal tract, vocal tract anatomy, articulator actions, and how these components can be combined, readers will understand and recreate the process by which language is learned. They will then be introduced to the International Phonetic Alphabet (IPA) and will practice using the IPA for narrow transcription of speech sounds. This revised second edition features:  New and updated exercises exploring vowel charts More pathways for connecting speechwork with acting work, including professional applications Updated images throughout Updated information for instructors and the use of the text in various courses New web resources, including additional exercise and video demos. Experiencing Speech: A Skills-Based, Panlingual Approach to Actor Training is an excellent resource for teachers and students of speech and actor training, as well as aspiring actors looking to diversify their speech skills.

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]

The phonetic engine is a system that performs speech signal to symbol transformation. This work describes some issues in the development of an Assamese Phonetic Engine (PE). International phonetic alphabet (IPA) is used as the phonetic unit to transcribe the speech database collected in three different modes, namely, reading, lecture and conversation modes. Only reading mode data is used for training and Hidden markov model (HMM) is used to model each phonetic unit without imposing any language or contextual constraint. The trained HMMs are used to derive a sequence of phonetic units from a test speech signal. Accuracy of 47.31%, 45.30% and 36.13% is achieved in reading, lecture and conversation mode, respectively. Confusion among the phonetic units specific to Assamese are discussed. Issues related to different recording modes, language and native speaker dependencies are discussed. The speech data is also collected in Hindi from three different sets of speakers to study speaker, language and native dependancies. Accuracy of 40.5%, 36.10% and 29.61% is achieved in native speaker dependent, native speaker independent and non-native speaker independent cases, respectively.

This paper discusses development of resources using linguistics and signal processing aspects for two low resource Indian languages, viz., Gujarati and Marathi. Speech resource development discusses the details of data collection, transcription at phone and syllable level and corresponding linguistic units such as phones and syllables. In order to analyze the performance at different fluency levels, three types of recording modes, viz., read, conversation and lecture are considered in this paper. Manual annotation of speech in terms of International Phonetic Alphabet (IPA) symbols is presented. In the later section, we discuss speech segmentation at syllable level and prosodic level marking (pitch marking). Short-term Energy contour is smoothened using group-delay-based algorithm in order to detect syllable units in the speech signal. Detection rate obtained for syllable marking within 20 % agreement duration is of the order of 60 % in case of read mode speech. Prosody pitch marks are analyzed via Fo pattern of a speech signal. The key strength of this study is the analysis for different kinds of recording modes, viz., read, conversation and lecture mode. It is found that CV (where, Consonant is followed by Vowel) type of syllables have highest occurrence (more than 50 %) in both the languages. Read speech is observed to perform better than spontaneous speech in terms of automatic prosodic marking.

The introduction of a phonetic engine in the literature is that it is a system which transforms a speech signal into some symbolic form. Phone recognition is a primary task of a PE. Among the various other applications of PE, one very useful application is the Language Identification (LID). This chapter discusses some issues pertaining to the use of PE in phone recognition as well as in language identification. Here, we have used PEs for three Indian Languages: Manipuri, Assamese and Bengali, in building the LID system. These languages are widely spoken in the Northeastern region of India. In the development of PEs, the International Phonetic Alphabet (IPA) symbols are used in the data transcription process. In modeling the phonetic units Hidden Markov Models (HMM) have been used in the training phase. These trained HMMs are then used in phone recognitions leading to the identification of language(s) of unknown test utterances. The overall phone recognition accuracies reported by the existing PEs for the above selected languages are \(62.11\%\) for standard Manipuri language, \(59.0\%\) for Kakching Dialect of Manipuri, \(43.28\%\) for standard Assamese and \(48.58\%\) for Bengali language. Automatic LID is possible using a set of PEs in testing unknown utterances due to the language bias of these systems. Various levels of identification rates reported in some LID tasks carried out with PEs are discussed here to look into the issues belonging to it.

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.