Phonemic Similarity Metrics to Compare Pronunciation Methods
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Rule-Based Standard Arabic Phonetization at Phoneme, Allophone, and Syllable Level
Fadi Sindran, Firas Mualla, Tino Haderlein, Khaled Daqrouq & Elmar Nöth Rule-Based Standard Arabic Phonetization at Phoneme, Allophone, and Syllable Level Fadi Sindran [email protected] Faculty of Engineering Department of Computer Science /Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, 91058, Germany Firas Mualla [email protected] Faculty of Engineering Department of Computer Science /Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, 91058, Germany Tino Haderlein [email protected] Faculty of Engineering Department of Computer Science/Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, 91058, Germany Khaled Daqrouq [email protected] Department of Electrical and Computer Engineering King Abdulaziz University, Jeddah, 22254, Saudi Arabia Elmar Nöth [email protected] Faculty of Engineering Department of Computer Science /Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, 91058, Germany Abstract Phonetization is the transcription from written text into sounds. It is used in many natural language processing tasks, such as speech processing, speech synthesis, and computer-aided pronunciation assessment. A common phonetization approach is the use of letter-to-sound rules developed by linguists for the transcription from grapheme to sound. In this paper, we address the problem of rule-based phonetization of standard Arabic. 1The paper contributions can be summarized as follows: 1) Discussion of the transcription rules of standard Arabic which were used in literature on the phonemic and phonetic level. 2) Improvements of existing rules are suggested and new rules are introduced. Moreover, a comprehensive algorithm covering the phenomenon of pharyngealization in standard Arabic is proposed. -
Problem of Creating a Professional Dictionary of Uncodified Vocabulary
Utopía y Praxis Latinoamericana ISSN: 1315-5216 ISSN: 2477-9555 [email protected] Universidad del Zulia Venezuela Problem of Creating a Professional Dictionary of Uncodified Vocabulary MOROZOVA, O.A; YAKHINA, A.M; PESTOVA, M.S Problem of Creating a Professional Dictionary of Uncodified Vocabulary Utopía y Praxis Latinoamericana, vol. 25, no. Esp.7, 2020 Universidad del Zulia, Venezuela Available in: https://www.redalyc.org/articulo.oa?id=27964362018 DOI: https://doi.org/10.5281/zenodo.4009661 This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. PDF generated from XML JATS4R by Redalyc Project academic non-profit, developed under the open access initiative O.A MOROZOVA, et al. Problem of Creating a Professional Dictionary of Uncodified Vocabulary Artículos Problem of Creating a Professional Dictionary of Uncodified Vocabulary Problema de crear un diccionario profesional de vocabulario no codificado O.A MOROZOVA DOI: https://doi.org/10.5281/zenodo.4009661 Kazan Federal University, Rusia Redalyc: https://www.redalyc.org/articulo.oa? [email protected] id=27964362018 http://orcid.org/0000-0002-4573-5858 A.M YAKHINA Kazan Federal University, Rusia [email protected] http://orcid.org/0000-0002-8914-0995 M.S PESTOVA Ural State Law University, Rusia [email protected] http://orcid.org/0000-0002-7996-9636 Received: 03 August 2020 Accepted: 10 September 2020 Abstract: e article considers the problem of lexicographical fixation of uncodified vocabulary in a professional dictionary. e oil sublanguage, being one of the variants of common language realization used by a limited group of its medium in conditions of official and also non-official communication, provides interaction of people employed in the oil industry. -
Towards a Conceptual Representation of Lexical Meaning in Wordnet
Towards a Conceptual Representation of Lexical Meaning in WordNet Jen-nan Chen Sue J. Ker Department of Information Management, Department of Computer Science, Ming Chuan University Soochow University Taipei, Taiwan Taipei, Taiwan [email protected] [email protected] Abstract Knowledge acquisition is an essential and intractable task for almost every natural language processing study. To date, corpus approach is a primary means for acquiring lexical-level semantic knowledge, but it has the problem of knowledge insufficiency when training on various kinds of corpora. This paper is concerned with the issue of how to acquire and represent conceptual knowledge explicitly from lexical definitions and their semantic network within a machine-readable dictionary. Some information retrieval techniques are applied to link between lexical senses in WordNet and conceptual ones in Roget's categories. Our experimental results report an overall accuracy of 85.25% (87, 78, 89, and 87% for nouns, verbs, adjectives and adverbs, respectively) when evaluating on polysemous words discussed in previous literature. 1 Introduction Knowledge representation has traditionally been thought of as the heart of various applications of natural language processing. Anyone who has built a natural language processing system has had to tackle the problem of representing its knowledge of the world. One of the applications for knowledge representation is word sense disambiguation (WSD) for unrestricted text, which is one of the major problems in analyzing sentences. In the past, the substantial literature on WSD has concentrated on statistical approaches to analyzing and extracting information from corpora (Gale et al. 1992; Yarowsky 1992, 1995; Dagan and Itai 1994; Luk 1995; Ng and Lee 1996). -
Lecture # 07 (Phonetics)
Phonetics Phonetics (Introduction), Syllables, - text-to-speech recognition, - Onset, nucleus - Phones, - Coda, Rime, Speech sounds and Phonetic - Consonants and vowels, transcription, Phonological Categories and - International Phonetic Alphabet Pronunciation variation, (IPA), - ARPAbet, - Phonetic features, Articulatory Phonetics, - Predicting Phonetic variation, - The Vocal organs, - Factors Influencing Phonetic - Consonants: Place of Articulation, variation, - Consonants: Manner of Articulation, - Vowels, - Syllables, @Copyrights: Natural Language Processing (NLP) Organized by Dr. Ahmad Jalal (http://portals.au.edu.pk/imc/) 1. Phonetics (Introduction) “ What is the proper pronunciation of word “Special”” Text-to-speech conversation (converting strings of text words into acoustic waveforms). “Phonics” methods of teaching to children; - is first glance like a purely modern educational debate. Phonetics is the study of linguistic sounds, - how they are produced by the articulators of the human vocal tract, - how they are realized acoustically, - and how this acoustic realization can be digitized and processed. A key element of both speech recognition and text-to-speech systems: - how words are pronounced in terms of individual speech units called phones. @Copyrights: Natural Language Processing (NLP) Organized by Dr. Ahmad Jalal (http://portals.au.edu.pk/imc/) 2. Speech Sounds and Phonetic Transcription The study of the pronunciation of words is part of the field of phonetics, We model the pronunciation of a word as a string of symbols which represent phones or segments. - A phone is a speech sound; - phones are represented with phonetic symbols that bear some resemblance to a letter. We use 2 different alphabets for describing phones as; (1) The International Phonetic Alphabet (IPA) - is an evolving standard originally developed by the International Phonetic Association in 1888 with the goal of transcribing the sounds of all human languages. -
Inferring Parts of Speech for Lexical Mappings Via the Cyc KB
to appear in Proceeding 20th International Conference on Computational Linguistics (COLING-04), August 23-27, 2004 Geneva Inferring parts of speech for lexical mappings via the Cyc KB Tom O’Hara‡, Stefano Bertolo, Michael Witbrock, Bjørn Aldag, Jon Curtis,withKathy Panton, Dave Schneider,andNancy Salay ‡Computer Science Department Cycorp, Inc. New Mexico State University Austin, TX 78731 Las Cruces, NM 88001 {bertolo,witbrock,aldag}@cyc.com [email protected] {jonc,panton,daves,nancy}@cyc.com Abstract in the phrase “painting for sale.” In cases like this, semantic or pragmatic criteria, as opposed We present an automatic approach to learn- to syntactic criteria only, are necessary for deter- ing criteria for classifying the parts-of-speech mining the proper part of speech. The headword used in lexical mappings. This will fur- part of speech is important for correctly identifying ther automate our knowledge acquisition sys- phrasal variations. For instance, the first term can tem for non-technical users. The criteria for also occur in the same sense in “paint for money.” the speech parts are based on the types of However, this does not hold for the second case, the denoted terms along with morphological since “paint for sale” has an entirely different sense and corpus-based clues. Associations among (i.e., a substance rather than an artifact). these and the parts-of-speech are learned us- When lexical mappings are produced by naive ing the lexical mappings contained in the Cyc users, such as in DARPA’s Rapid Knowledge For- knowledge base as training data. With over mation (RKF) project, it is desirable that tech- 30 speech parts to choose from, the classifier nical details such as the headword part of speech achieves good results (77.8% correct). -
Symbols Used in the Dictionary
Introduction 1. This dictionary will help you to be able to use Einglish - English Dictionary well & use it in your studying. 2. The exercies will provide you with what information your dictionary contains & how to find as well use them quickly & correctly. So you MUST: 1. make sure you understand the instructuion of the exercise. 2. check on yourself by working out examples, using your dictionay. 3. Do it carefully but quickly as you can even if you think you know the answer. 4. always scan the whole entry first to get the general idea, then find the part that you’re interested in. 5. Read slowly & carefully. 6. Help to make the learning process interesting. 7. If you make a mistake, find out why. Remember, the more you use the dictionary, the more uses you will find for it. 1 Chapter one Dictionary Entry • Terms used in the Dictionary. •Symbols used in the Dictionary. •Different typfaces used in the Dictionary. •Abbreviation used in the Dictionary. 2 1- Terms used in the Dictionay: -What is Headword? -What is an Entry? -What is a Definition? -What is a Compound? -What is a Derivative? -What information does an entry contain? 3 1- What is Headword? It’s the word you look up in a dictionary. Also, it’s the name given to each of the words listed alphabetically. 4 2- What is an Entry? It consists of a headword & all the information about the headword. 5 3- What is a Definition? It’s a part of an entry which describes a particular meaning & usage of the headword. -
DICTIONARY News
Number 17 y July 2009 Kernerman kdictionaries.com/kdn DICTIONARY News KD’s BLDS: a brief introduction In 2005 K Dictionaries (KD) entered a project of developing We started by establishing an extensive infrastructure both dictionaries for learners of different languages. KD had already contentwise and technologically. The lexicographic compilation created several non-English titles a few years earlier, but those was divided into 25 projects: 1 for the French core, 8 for the were basic word-to-word dictionaries. The current task marked dictionary cores of the eight languages, another 8 for the a major policy shift for our company since for the first time we translation from French to eight languages, and 8 more for the were becoming heavily involved in learner dictionaries with translation of each language to French. target languages other than English. That was the beginning of An editorial team was set up for developing each of the nine our Bilingual Learners Dictionaries Series (BLDS), which so (French + 8) dictionary cores. The lexicographers worked from far covers 20 different language cores and keeps growing. a distance, usually at home, all over the world. The chief editor The BLDS was launched with a program for eight French for each language was responsible for preparing the editorial bilingual dictionaries together with Assimil, a leading publisher styleguide and the list of headwords. Since no corpora were for foreign language learning in France which made a strategic publicly available for any of these languages, each editor used decision to expand to dictionaries in cooperation with KD. different means to retrieve information in order to compile the The main target users were identified as speakers of French headword list. -
UC Berkeley Dissertations, Department of Linguistics
UC Berkeley Dissertations, Department of Linguistics Title Relationship between Perceptual Accuracy and Information Measures: A cross-linguistic Study Permalink https://escholarship.org/uc/item/7tx5t8bt Author Kang, Shinae Publication Date 2015 eScholarship.org Powered by the California Digital Library University of California Relationship between perceptual accuracy and information measures: A cross-linguistic study by Shinae Kang A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Linguistics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Keith A. Johnson, Chair Professor Sharon Inkelas Professor Susan S. Lin Professor Robert T. Knight Fall 2015 Relationship between perceptual accuracy and information measures: A cross-linguistic study Copyright 2015 by Shinae Kang 1 Abstract Relationship between perceptual accuracy and information measures: A cross-linguistic study by Shinae Kang Doctor of Philosophy in Linguistics University of California, Berkeley Professor Keith A. Johnson, Chair The current dissertation studies how the information conveyed by different speech el- ements of English, Japanese and Korean correlates with perceptual accuracy. Two well- established information measures are used: weighted negative contextual predictability (in- formativity) of a speech element; and the contribution of a speech element to syllable differ- entiation, or functional load. This dissertation finds that the correlation between information and perceptual accuracy differs depending on both the type of information measure and the language of the listener. To compute the information measures, Chapter 2 introduces a new corpus consisting of all the possible syllables for each of the three languages. The chapter shows that the two information measures are inversely correlated. -
Japanese-English Cross-Language Headword Search of Wikipedia
Japanese-English Cross-Language Headword Search of Wikipedia Satoshi Sato and Masaya Okada Graduate School of Engineering Nagoya University Chikusa-ku, Nagoya, Japan [email protected] masaya [email protected] Abstract glish article. To realize CLHS, term translation is required. This paper describes a Japanese-English cross-language headword search system of Term translation is not a mainstream of machine Wikipedia, which enables to find an appro- translation research, because a simple dictionary- priate English article from a given Japanese based method is widely used and enough for query term. The key component of the sys- general terms. For technical terms and proper tem is a term translator, which selects an ap- nouns, automatic extraction of translation pairs propriate English headword among the set from bilingual corpora and the Web has been in- of headwords in English Wikipedia, based on the framework of non-productive ma- tensively studied (e.g., (Daille et al., 1994) and chine translation. An experimental result (Lin et al., 2008)) in order to fill the lack of en- shows that the translation performance of tries in a bilingual dictionary. However, the qual- our system is equal or slightly better than ity of automatic term translation is not well exam- commercial machine translation systems, ined with a few exception (Baldwin and Tanaka, Google translate and Mac-Transer. 2004). Query translation in cross-language infor- mation retrieval is related to term translation, but it concentrates on translation of content words in 1 Introduction a query (Fujii and Ishikawa, 2001). Many people use Wikipedia, an encyclopedia on In the CLHS situation, the ultimate goal of the Web, to find meanings of unknown terms. -
Download in the Conll-Format and Comprise Over ±175,000 Tokens
Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning Mike Kestemont1, Jeroen De Gussem2 1 University of Antwerp, Belgium 2 Ghent University, Belgium *Corresponding author: [email protected] Abstract In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is typical of medieval Latin. In Digital Classics, these tasks are traditionally solved in a (i) cascaded and (ii) lexicon-dependent fashion. For example, a lexicon is used to generate all the potential lemma-tag pairs for a token, and next, a context-aware PoS-tagger is used to select the most appropriate tag-lemma pair. Apart from the problems with out-of-lexicon items, error percolation is a major downside of such approaches. In this paper we explore the possibility to elegantly solve these tasks using a single, integrated approach. For this, we make use of a layered neural network architecture from the field of deep representation learning. keywords tagging, lemmatization, medieval Latin, LSTM, deep learning INTRODUCTION Latin —and its historic variants in particular— have long been a topic of major interest in Natural Language Processing [Piotrowksi 2012]. Especially in the community of Digital Humanities, the automated processing of Latin texts has always been a popular research topic. In a variety of computational applications, such as text re-use detection [Franzini et al, 2015], it is desirable to annotate and augment Latin texts with useful morpho-syntactical or lexical information, such as lemmas. -
Multilingual Speech Recognition for Selected West-European Languages
master’s thesis Multilingual Speech Recognition for Selected West-European Languages Bc. Gloria María Montoya Gómez May 25, 2018 advisor: Doc. Ing. Petr Pollák, CSc. Czech Technical University in Prague Faculty of Electrical Engineering, Department of Circuit Theory Acknowledgement I would like to thank my advisor, Doc. Ing. Petr Pollák, CSc. I was very privileged to have him as my mentor. Pollák was always friendly and patient to give me his knowl- edgeable advise and encouragement during my study at Czech Technical University in Prague. His high standards on quality taught me how to do good scientific work, write, and present ideas. Declaration I declare that I worked out the presented thesis independently and I quoted all used sources of information in accord with Methodical instructions about ethical principles for writing academic thesis. iii Abstract Hlavním cílem předložené práce bylo vytvoření první verze multilingválního rozpozná- vače řeči pro vybrané 4 západoevropské jazyky. Klíčovým úkolem této práce bylo de- finovat vztahy mezi subslovními akustickými elementy napříč jednotlivými jazyky při tvorbě automatického rozpoznávače řeči pro více jazyků. Vytvořený multilingvální sys- tém pokrývá aktuálně následující jazyky: angličtinu, němčinu, portugalštinu a španěl- štinu. Jelikož dostupná fonetická reprezentace hlásek pro jednotlivé jazyky byla různá podle použitých zdrojových dat, prvním krokem této práce bylo její sjednocení a vy- tvoření sdílené fonetické reprezentace na bázi abecedy X-SAMPA. Pokud jsou dále acoustické subslovní elementy reprezentovány sdílenými skrytými Markovovy modely, případný nedostatek zdrojových dat pro trénováni může být pokryt z jiných jazyků. Dalším krokem byla vlastní realizace multilingválního systému pomocí nástrojové sady KALDI. Použité jazykové modely byly na bázi zredukovaných trigramových modelů zís- kaných z veřejně dostupých zdrojů. -
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars
Towards Structuring an Arabic-English Machine-Readable Dictionary Using Parsing Expression Grammars Diaa Mohamed Fayed1 Aly Aly Fahmy2 Mohsen Abdelrazek Rashwan3 Wafaa Kamel Fayed4 1 Department of Computer Science, Faculty of Computers and Information, 2 Department of Computer Science, Faculty of Computers and Information, 3 Department of EECE, Faculty of Engineering, 4 Department of Arabic Language and Literatures, Faculty of Arts, Cairo University, [email protected], [email protected], [email protected], [email protected] ABSTRACT: Dictionaries are rich sources of lexical information about words that is required for many applications of human language technology. However, publishers prepare printed dictionaries for human usage not for machine processing. This paper presents a method to structure partly a machine-readable version of the Arabic-English Al-Mawrid dictionary. The method converted the entries of Al-Mawrid from a stream of words and punctuation marks into hierarchical structures. The hierarchical structure or tree structure expresses the components of each dictionary entry in explicit format. The components of an entry are subentries and each subentry consists of defining phrases, domain labels, cross-references, and translation equivalences. We designed the proposed method as cascaded steps where parsing is the main step. We implemented the parser through the parsing expression grammar formalism. In conclusion, although Arabic dictionaries do not have microstructure standardization, this study demonstrated that it is possible to structure them automatically or semi- automatically with plausible accuracy after inducing their microstructure. Keywords: Al-Mawrid, Arabic-English, Machine-Readable Dictionary, Parsing Expression Grammars Received: 16 November 2013, Revised 19 December 2013, Accepted 24 December 2013 © 2014 DLINE.