Chapter 8 Speech Synthesis
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THE DEVELOPMENT of ACCENTED ENGLISH SYNTHETIC VOICES By
THE DEVELOPMENT OF ACCENTED ENGLISH SYNTHETIC VOICES by PROMISE TSHEPISO MALATJI DISSERTATION Submitted in fulfilment of the requirements for the degree of MASTER OF SCIENCE in COMPUTER SCIENCE in the FACULTY OF SCIENCE AND AGRICULTURE (School of Mathematical and Computer Sciences) at the UNIVERSITY OF LIMPOPO SUPERVISOR: Mr MJD Manamela CO-SUPERVISOR: Dr TI Modipa 2019 DEDICATION In memory of my grandparents, Cecilia Khumalo and Alfred Mashele, who always believed in me! ii DECLARATION I declare that THE DEVELOPMENT OF ACCENTED ENGLISH SYNTHETIC VOICES is my own work and that all the sources that I have used or quoted have been indicated and acknowledged by means of complete references and that this work has not been submitted before for any other degree at any other institution. ______________________ ___________ Signature Date iii ACKNOWLEDGEMENTS I want to recognise the following people for their individual contributions to this dissertation: • My brother, Mr B.I. Khumalo and the whole family for the unconditional love, support and understanding. • A distinct thank you to both my supervisors, Mr M.J.D. Manamela and Dr T.I. Modipa, for their guidance, motivation, and support. • The Telkom Centre of Excellence for Speech Technology for providing the resources and support to make this study a success. • My colleagues in Department of Computer Science, Messrs V.R. Baloyi and L.M. Kola, for always motivating me. • A special thank you to Mr T.J. Sefara for taking his time to participate in the study. • The six Computer Science undergraduate students who sacrificed their precious time to participate in data collection. -
The Role of Higher-Level Linguistic Features in HMM-Based Speech Synthesis
INTERSPEECH 2010 The role of higher-level linguistic features in HMM-based speech synthesis Oliver Watts, Junichi Yamagishi, Simon King Centre for Speech Technology Research, University of Edinburgh, UK [email protected] [email protected] [email protected] Abstract the annotation of a synthesiser’s training data on listeners’ re- action to the speech produced by that synthesiser is still unclear We analyse the contribution of higher-level elements of the lin- to us. In particular, we suspect that features such as pitch ac- guistic specification of a data-driven speech synthesiser to the cent type which might be useful in voice-building if labelled naturalness of the synthetic speech which it generates. The reliably, are under-used in conventional systems because of la- system is trained using various subsets of the full feature-set, belling/prediction errors. For building the systems to be evalu- in which features relating to syntactic category, intonational ated we therefore used data for which hand labelled annotation phrase boundary, pitch accent and boundary tones are selec- of ToBI events is available. This allows us to compare ideal sys- tively removed. Utterances synthesised by the different config- tems built from a corpus where these higher-level features are urations of the system are then compared in a subjective evalu- accurately annotated with more conventional systems that rely ation of their naturalness. exclusively on prediction of these features from text for annota- The work presented forms background analysis for an on- tion of their training data. going set of experiments in performing text-to-speech (TTS) conversion based on shallow features: features that can be triv- 2. -
Compound Word Formation.Pdf
Snyder, William (in press) Compound word formation. In Jeffrey Lidz, William Snyder, and Joseph Pater (eds.) The Oxford Handbook of Developmental Linguistics . Oxford: Oxford University Press. CHAPTER 6 Compound Word Formation William Snyder Languages differ in the mechanisms they provide for combining existing words into new, “compound” words. This chapter will focus on two major types of compound: synthetic -ER compounds, like English dishwasher (for either a human or a machine that washes dishes), where “-ER” stands for the crosslinguistic counterparts to agentive and instrumental -er in English; and endocentric bare-stem compounds, like English flower book , which could refer to a book about flowers, a book used to store pressed flowers, or many other types of book, as long there is a salient connection to flowers. With both types of compounding we find systematic cross- linguistic variation, and a literature that addresses some of the resulting questions for child language acquisition. In addition to these two varieties of compounding, a few others will be mentioned that look like promising areas for coordinated research on cross-linguistic variation and language acquisition. 6.1 Compounding—A Selective Review 6.1.1 Terminology The first step will be defining some key terms. An unfortunate aspect of the linguistic literature on morphology is a remarkable lack of consistency in what the “basic” terms are taken to mean. Strictly speaking one should begin with the very term “word,” but as Spencer (1991: 453) puts it, “One of the key unresolved questions in morphology is, ‘What is a word?’.” Setting this grander question to one side, a word will be called a “compound” if it is composed of two or more other words, and has approximately the same privileges of occurrence within a sentence as do other word-level members of its syntactic category (N, V, A, or P). -
Hunspell – the Free Spelling Checker
Hunspell – The free spelling checker About Hunspell Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex word compounding or character encoding. Hunspell interfaces: Ispell-like terminal interface using Curses library, Ispell pipe interface, OpenOffice.org UNO module. Main features of Hunspell spell checker and morphological analyzer: - Unicode support (affix rules work only with the first 65535 Unicode characters) - Morphological analysis (in custom item and arrangement style) and stemming - Max. 65535 affix classes and twofold affix stripping (for agglutinative languages, like Azeri, Basque, Estonian, Finnish, Hungarian, Turkish, etc.) - Support complex compoundings (for example, Hungarian and German) - Support language specific features (for example, special casing of Azeri and Turkish dotted i, or German sharp s) - Handle conditional affixes, circumfixes, fogemorphemes, forbidden words, pseudoroots and homonyms. - Free software (LGPL, GPL, MPL tri-license) Usage The src/tools dictionary contains ten executables after compiling (or some of them are in the src/win_api): affixcompress: dictionary generation from large (millions of words) vocabularies analyze: example of spell checking, stemming and morphological analysis chmorph: example of automatic morphological generation and conversion example: example of spell checking and suggestion hunspell: main program for spell checking and others (see manual) hunzip: decompressor of hzip format hzip: compressor of -
The RACAI Text-To-Speech Synthesis System
The RACAI Text-to-Speech Synthesis System Tiberiu Boroș, Radu Ion, Ștefan Daniel Dumitrescu Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy (RACAI) [email protected], [email protected], [email protected] of standalone Natural Language Processing (NLP) Tools Abstract aimed at enabling text-to-speech (TTS) synthesis for less- This paper describes the RACAI Text-to-Speech (TTS) entry resourced languages. A good example is the case of for the Blizzard Challenge 2013. The development of the Romanian, a language which poses a lot of challenges for TTS RACAI TTS started during the Metanet4U project and the synthesis mainly because of its rich morphology and its system is currently part of the METASHARE platform. This reduced support in terms of freely available resources. Initially paper describes the work carried out for preparing the RACAI all the tools were standalone, but their design allowed their entry during the Blizzard Challenge 2013 and provides a integration into a single package and by adding a unit selection detailed description of our system and future development speech synthesis module based on the Pitch Synchronous directions. Overlap-Add (PSOLA) algorithm we were able to create a fully independent text-to-speech synthesis system that we refer Index Terms: speech synthesis, unit selection, concatenative to as RACAI TTS. 1. Introduction A considerable progress has been made since the start of the project, but our system still requires development and Text-to-speech (TTS) synthesis is a complex process that although considered premature, our participation in the addresses the task of converting arbitrary text into voice. -
Synthesis and Recognition of Speech Creating and Listening to Speech
ISSN 1883-1974 (Print) ISSN 1884-0787 (Online) National Institute of Informatics News NII Interview 51 A Combination of Speech Synthesis and Speech Oct. 2014 Recognition Creates an Affluent Society NII Special 1 “Statistical Speech Synthesis” Technology with a Rapidly Growing Application Area NII Special 2 Finding Practical Application for Speech Recognition Feature Synthesis and Recognition of Speech Creating and Listening to Speech A digital book version of “NII Today” is now available. http://www.nii.ac.jp/about/publication/today/ This English language edition NII Today corresponds to No. 65 of the Japanese edition [Advance Notice] Great news! NII Interview Yamagishi-sensei will create my voice! Yamagishi One result is a speech translation sys- sound. Bit (NII Character) A Combination of Speech Synthesis tem. This system recognizes speech and translates it Ohkawara I would like your comments on the fu- using machine translation to synthesize speech, also ture challenges. and Speech Recognition automatically translating it into every language to Ono The challenge for speech recognition is how speak. Moreover, the speech is created with a human close it will come to humans in the distant speech A Word from the Interviewer voice. In second language learning, you can under- case. If this study is advanced, it will be possible to Creates an Affluent Society stand how you should pronounce it with your own summarize the contents of a meeting and to automati- voice. If this is further advanced, the system could cally take the minutes. If a robot understands the con- have an actor in a movie speak in a different language tents of conversations by multiple people in a natural More and more people have begun to use smart- a smartphone, I use speech input more often. -
Building a Treebank for French
Building a treebank for French £ £¥ Anne Abeillé£ , Lionel Clément , Alexandra Kinyon ¥ £ TALaNa, Université Paris 7 University of Pennsylvania 75251 Paris cedex 05 Philadelphia FRANCE USA abeille, clement, [email protected] Abstract Very few gold standard annotated corpora are currently available for French. We present an ongoing project to build a reference treebank for French starting with a tagged newspaper corpus of 1 Million words (Abeillé et al., 1998), (Abeillé and Clément, 1999). Similarly to the Penn TreeBank (Marcus et al., 1993), we distinguish an automatic parsing phase followed by a second phase of systematic manual validation and correction. Similarly to the Prague treebank (Hajicova et al., 1998), we rely on several types of morphosyntactic and syntactic annotations for which we define extensive guidelines. Our goal is to provide a theory neutral, surface oriented, error free treebank for French. Similarly to the Negra project (Brants et al., 1999), we annotate both constituents and functional relations. 1. The tagged corpus pronoun (= him ) or a weak clitic pronoun (= to him or to As reported in (Abeillé and Clément, 1999), we present her), plus can either be a negative adverb (= not any more) the general methodology, the automatic tagging phase, the or a simple adverb (= more). Inflectional morphology also human validation phase and the final state of the tagged has to be annotated since morphological endings are impor- corpus. tant for gathering constituants (based on agreement marks) and also because lots of forms in French are ambiguous 1.1. Methodology with respect to mode, person, number or gender. For exam- 1.1.1. -
The Role of Speech Processing in Human-Computer Intelligent Communication
THE ROLE OF SPEECH PROCESSING IN HUMAN-COMPUTER INTELLIGENT COMMUNICATION Candace Kamm, Marilyn Walker and Lawrence Rabiner Speech and Image Processing Services Research Laboratory AT&T Labs-Research, Florham Park, NJ 07932 Abstract: We are currently in the midst of a revolution in communications that promises to provide ubiquitous access to multimedia communication services. In order to succeed, this revolution demands seamless, easy-to-use, high quality interfaces to support broadband communication between people and machines. In this paper we argue that spoken language interfaces (SLIs) are essential to making this vision a reality. We discuss potential applications of SLIs, the technologies underlying them, the principles we have developed for designing them, and key areas for future research in both spoken language processing and human computer interfaces. 1. Introduction Around the turn of the twentieth century, it became clear to key people in the Bell System that the concept of Universal Service was rapidly becoming technologically feasible, i.e., the dream of automatically connecting any telephone user to any other telephone user, without the need for operator assistance, became the vision for the future of telecommunications. Of course a number of very hard technical problems had to be solved before the vision could become reality, but by the end of 1915 the first automatic transcontinental telephone call was successfully completed, and within a very few years the dream of Universal Service became a reality in the United States. We are now in the midst of another revolution in communications, one which holds the promise of providing ubiquitous service in multimedia communications. -
Gender, Ethnicity, and Identity in Virtual
Virtual Pop: Gender, Ethnicity, and Identity in Virtual Bands and Vocaloid Alicia Stark Cardiff University School of Music 2018 Presented in partial fulfilment of the requirements for the degree Doctor of Philosophy in Musicology TABLE OF CONTENTS ABSTRACT i DEDICATION iii ACKNOWLEDGEMENTS iv INTRODUCTION 7 EXISTING STUDIES OF VIRTUAL BANDS 9 RESEARCH QUESTIONS 13 METHODOLOGY 19 THESIS STRUCTURE 30 CHAPTER 1: ‘YOU’VE COME A LONG WAY, BABY:’ THE HISTORY AND TECHNOLOGIES OF VIRTUAL BANDS 36 CATEGORIES OF VIRTUAL BANDS 37 AN ANIMATED ANTHOLOGY – THE RISE IN POPULARITY OF ANIMATION 42 ALVIN AND THE CHIPMUNKS… 44 …AND THEIR SUCCESSORS 49 VIRTUAL BANDS FOR ALL AGES, AVAILABLE ON YOUR TV 54 VIRTUAL BANDS IN OTHER TYPES OF MEDIA 61 CREATING THE VOICE 69 REPRODUCING THE BODY 79 CONCLUSION 86 CHAPTER 2: ‘ALMOST UNREAL:’ TOWARDS A THEORETICAL FRAMEWORK FOR VIRTUAL BANDS 88 DEFINING REALITY AND VIRTUAL REALITY 89 APPLYING THEORIES OF ‘REALNESS’ TO VIRTUAL BANDS 98 UNDERSTANDING MULTIMEDIA 102 APPLYING THEORIES OF MULTIMEDIA TO VIRTUAL BANDS 110 THE VOICE IN VIRTUAL BANDS 114 AGENCY: TRANSFORMATION THROUGH TECHNOLOGY 120 CONCLUSION 133 CHAPTER 3: ‘INSIDE, OUTSIDE, UPSIDE DOWN:’ GENDER AND ETHNICITY IN VIRTUAL BANDS 135 GENDER 136 ETHNICITY 152 CASE STUDIES: DETHKLOK, JOSIE AND THE PUSSYCATS, STUDIO KILLERS 159 CONCLUSION 179 CHAPTER 4: ‘SPITTING OUT THE DEMONS:’ GORILLAZ’ CREATION STORY AND THE CONSTRUCTION OF AUTHENTICITY 181 ACADEMIC DISCOURSE ON GORILLAZ 187 MASCULINITY IN GORILLAZ 191 ETHNICITY IN GORILLAZ 200 GORILLAZ FANDOM 215 CONCLUSION 225 -
Voice User Interface on the Web Human Computer Interaction Fulvio Corno, Luigi De Russis Academic Year 2019/2020 How to Create a VUI on the Web?
Voice User Interface On The Web Human Computer Interaction Fulvio Corno, Luigi De Russis Academic Year 2019/2020 How to create a VUI on the Web? § Three (main) steps, typically: o Speech Recognition o Text manipulation (e.g., Natural Language Processing) o Speech Synthesis § We are going to start from a simple application to reach a quite complex scenario o by using HTML5, JS, and PHP § Reminder: we are interested in creating an interactive prototype, at the end 2 Human Computer Interaction Weather Web App A VUI for "chatting" about the weather Base implementation at https://github.com/polito-hci-2019/vui-example 3 Human Computer Interaction Speech Recognition and Synthesis § Web Speech API o currently a draft, experimental, unofficial HTML5 API (!) o https://wicg.github.io/speech-api/ § Covers both speech recognition and synthesis o different degrees of support by browsers 4 Human Computer Interaction Web Speech API: Speech Recognition § Accessed via the SpeechRecognition interface o provides the ability to recogniZe voice from an audio input o normally via the device's default speech recognition service § Generally, the interface's constructor is used to create a new SpeechRecognition object § The SpeechGrammar interface can be used to represent a particular set of grammar that your app should recogniZe o Grammar is defined using JSpeech Grammar Format (JSGF) 5 Human Computer Interaction Speech Recognition: A Minimal Example const recognition = new window.SpeechRecognition(); recognition.onresult = (event) => { const speechToText = event.results[0][0].transcript; -
Masterarbeit
Masterarbeit Erstellung einer Sprachdatenbank sowie eines Programms zu deren Analyse im Kontext einer Sprachsynthese mit spektralen Modellen zur Erlangung des akademischen Grades Master of Science vorgelegt dem Fachbereich Mathematik, Naturwissenschaften und Informatik der Technischen Hochschule Mittelhessen Tobias Platen im August 2014 Referent: Prof. Dr. Erdmuthe Meyer zu Bexten Korreferent: Prof. Dr. Keywan Sohrabi Eidesstattliche Erklärung Hiermit versichere ich, die vorliegende Arbeit selbstständig und unter ausschließlicher Verwendung der angegebenen Literatur und Hilfsmittel erstellt zu haben. Die Arbeit wurde bisher in gleicher oder ähnlicher Form keiner anderen Prüfungsbehörde vorgelegt und auch nicht veröffentlicht. 2 Inhaltsverzeichnis 1 Einführung7 1.1 Motivation...................................7 1.2 Ziele......................................8 1.3 Historische Sprachsynthesen.........................9 1.3.1 Die Sprechmaschine.......................... 10 1.3.2 Der Vocoder und der Voder..................... 10 1.3.3 Linear Predictive Coding....................... 10 1.4 Moderne Algorithmen zur Sprachsynthese................. 11 1.4.1 Formantsynthese........................... 11 1.4.2 Konkatenative Synthese....................... 12 2 Spektrale Modelle zur Sprachsynthese 13 2.1 Faltung, Fouriertransformation und Vocoder................ 13 2.2 Phase Vocoder................................ 14 2.3 Spectral Model Synthesis........................... 19 2.3.1 Harmonic Trajectories........................ 19 2.3.2 Shape Invariance.......................... -
Expression Control in Singing Voice Synthesis
Expression Control in Singing Voice Synthesis Features, approaches, n the context of singing voice synthesis, expression control manipu- [ lates a set of voice features related to a particular emotion, style, or evaluation, and challenges singer. Also known as performance modeling, it has been ] approached from different perspectives and for different purposes, and different projects have shown a wide extent of applicability. The Iaim of this article is to provide an overview of approaches to expression control in singing voice synthesis. We introduce some musical applica- tions that use singing voice synthesis techniques to justify the need for Martí Umbert, Jordi Bonada, [ an accurate control of expression. Then, expression is defined and Masataka Goto, Tomoyasu Nakano, related to speech and instrument performance modeling. Next, we pres- ent the commonly studied set of voice parameters that can change and Johan Sundberg] Digital Object Identifier 10.1109/MSP.2015.2424572 Date of publication: 13 October 2015 IMAGE LICENSED BY INGRAM PUBLISHING 1053-5888/15©2015IEEE IEEE SIGNAL PROCESSING MAGAZINE [55] noVEMBER 2015 voices that are difficult to produce naturally (e.g., castrati). [TABLE 1] RESEARCH PROJECTS USING SINGING VOICE SYNTHESIS TECHNOLOGIES. More examples can be found with pedagogical purposes or as tools to identify perceptually relevant voice properties [3]. Project WEBSITE These applications of the so-called music information CANTOR HTTP://WWW.VIRSYN.DE research field may have a great impact on the way we inter- CANTOR DIGITALIS HTTPS://CANTORDIGITALIS.LIMSI.FR/ act with music [4]. Examples of research projects using sing- CHANTER HTTPS://CHANTER.LIMSI.FR ing voice synthesis technologies are listed in Table 1.