Relevant bibliographies by topics / Text-to-speech synthesis system

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Text-to-speech synthesis system'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Text-to-speech synthesis system"

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Lin, Kun‐Shan. "Text‐to‐speech synthesis system." Journal of the Acoustical Society of America 86, no. 5 (1989): 2051–52. http://dx.doi.org/10.1121/1.398486.

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Sunitha, Dr K. V. N., and P. Sunitha Devi. "Text Normalization for Telugu Text-to-Speech Synthesis." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 2 (2013): 2241–49. http://dx.doi.org/10.24297/ijct.v11i2.1176.

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Most areas related to language and speech technology, directly or indirectly, require handling of unrestricted text, and Text-to-speech systems directly need to work on real text. To build a natural sounding speech synthesis system, it is essential that the text processing component produce an appropriate sequence of phonemic units corresponding to an arbitrary input text. A novel approach is used, where the input text is tokenized, and classification is done based on token type. The token sense disambiguation is achieved by the semantic nature of the language and then the expansion rules are applied to get the normalized text. However, for Telugu language not much work is done on text normalization. In this paper we discuss our efforts for designing a rule based system to achieve text normalization in the context of building Telugu text-to-speech system.
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SPROAT, RICHARD. "Multilingual text analysis for text-to-speech synthesis." Natural Language Engineering 2, no. 4 (1996): 369–80. http://dx.doi.org/10.1017/s1351324997001654.

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We present a model of text analysis for text-to-speech (TTS) synthesis based on (weighted) finite state transducers, which serves as the text analysis module of the multilingual Bell Labs TTS system. The transducers are constructed using a lexical toolkit that allows declarative descriptions of lexicons, morphological rules, numeral-expansion rules, and phonological rules, inter alia. To date, the model has been applied to eight languages: Spanish, Italian, Romanian, French, German, Russian, Mandarin and Japanese.
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Hassana, IKANI Lucy, and MUHAMMAD SANUSI. "Text to Speech Synthesis System in Yoruba Language." International Journal of Advances in Scientific Research and Engineering 05, no. 10 (2019): 180–91. http://dx.doi.org/10.31695/ijasre.2019.33568.

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Shah, Krishna Bikram, Kiran Kumar Chaudhary, and Ashmita Ghimire. "Nepali Text to Speech Synthesis System using FreeTTS." SCITECH Nepal 13, no. 1 (2018): 24–31. http://dx.doi.org/10.3126/scitech.v13i1.23498.

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This paper confers the tools and methodology used in developing a Nepali Text to Speech Synthesis System using FreeTTS and is entirely developed in Java and uses FreeTTS synthesize1: Vocalized form of human communication is Speech. Here the Nepali Language is Synthetized based on formant approach and the use of one of the popular generic frameworks FreeTTS that is available in public domain for the development of a TTS system. The Text To Speech Architecture has been developed putting more emphasis on the Natural Language Processing (NLP) component rather than Digital Signal Processing (DSP) component. Nepali language being mostly used language in Nepal and some parts of India and abroad, a text-to-speech (TTS} synthesizer for this language will prove to be a convenient tool and communication technology (JCT) based system to aid to those majorities of people who are illiterate and also to those who are physical impairments like visually handicapped and vocally disabled persons. This ability to convert text to voice may reduce the dependency, frustration, and sense of helplessness of these people. The system can be extended to include more features such as emotions, improved tokenization, interactive options and the use of minimal database.
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Rebai, Ilyes, and Yassine BenAyed. "Text-to-speech synthesis system with Arabic diacritic recognition system." Computer Speech & Language 34, no. 1 (2015): 43–60. http://dx.doi.org/10.1016/j.csl.2015.04.002.

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Lin-Shan Lee, Chiu-Yu Tseng, and Ming Ouh-Young. "The synthesis rules in a Chinese text-to-speech system." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 9 (1989): 1309–20. http://dx.doi.org/10.1109/29.31286.

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Ahn, Seung-Kwon, and Koeng-Mo Sung. "Korean text-to-speech system using a formant synthesis method." Journal of the Acoustical Society of Japan (E) 13, no. 3 (1992): 151–60. http://dx.doi.org/10.1250/ast.13.151.

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Win, Kyawt Yin, and Tomio Takara. "Myanmar text-to-speech system with rule-based tone synthesis." Acoustical Science and Technology 32, no. 5 (2011): 174–81. http://dx.doi.org/10.1250/ast.32.174.

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Jacob, Agnes, and P. Mythili. "Developing a Child Friendly Text-to-Speech System." Advances in Human-Computer Interaction 2008 (2008): 1–6. http://dx.doi.org/10.1155/2008/597971.

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This paper discusses the implementation details of a child friendly, good quality, English text-to-speech (TTS) system that is phoneme-based, concatenative, easy to set up and use with little memory. Direct waveform concatenation and linear prediction coding (LPC) are used. Most existing TTS systems are unit-selection based, which use standard speech databases available in neutral adult voices. Here reduced memory is achieved by the concatenation of phonemes and by replacing phonetic wave files with their LPC coefficients. Linguistic analysis was used to reduce the algorithmic complexity instead of signal processing techniques. Sufficient degree of customization and generalization catering to the needs of the child user had been included through the provision for vocabulary and voice selection to suit the requisites of the child. Prosody had also been incorporated. This inexpensive TTS system was implemented in MATLAB, with the synthesis presented by means of a graphical user interface (GUI), thus making it child friendly. This can be used not only as an interesting language learning aid for the normal child but it also serves as a speech aid to the vocally disabled child. The quality of the synthesized speech was evaluated using the mean opinion score (MOS).
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Dissertations / Theses on the topic "Text-to-speech synthesis system"

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Micallef, Paul. "A text to speech synthesis system for Maltese." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/842702/.

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The subject of this thesis covers a considerably varied multidisciplinary area which needs to be addressed to be able to achieve a text-to-speech synthesis system of high quality, in any language. This is the first time that such a system has been built for Maltese, and therefore, there was the additional problem of no computerised sources or corpora. However many problems and much of the system designs are common to all languages. This thesis focuses on two general problems. The first is that of automatic labelling of phonemic data, since this is crucial for the setting up of Maltese speech corpora, which in turn can be used to improve the system. A novel way of achieving such automatic segmentation was investigated. This uses a mixed parameter model with maximum likelihood training of the first derivative of the features across a set of phonetic class boundaries. It was found that this gives good results even for continuous speech provided that a phonemic labelling of the text is available. A second general problem is that of segment concatenation, since the end and beginning of subsequent diphones can have mismatches in amplitude, frequency, phase and spectral envelope. The use of-intermediate frames, build up from the last and first frames of two concatenated diphones, to achieve a smoother continuity was analysed. The analysis was done both in time and in frequency. The use of wavelet theory for the separation of the spectral envelope from the excitation was also investigated. The linguistic system modules have been built for this thesis. In particular a rule based grapheme to phoneme conversion system that is serial and not hierarchical was developed. The morphological analysis required the design of a system which allowed two dissimilar lexical structures, (semitic and romance) to be integrated into one overall morphological analyser. Appendices at the back are included with detailed rules of the linguistic modules developed. The present system, while giving satisfactory intelligibility, with capability of modifying duration, does not include as yet a prosodic module.
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Baloyi, Ntsako. "A text-to-speech synthesis system for Xitsonga using hidden Markov models." Thesis, University of Limpopo (Turfloop Campus), 2012. http://hdl.handle.net/10386/1021.

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Thesis (M.Sc. (Computer Science) --University of Limpopo, 2013<br>This research study focuses on building a general-purpose working Xitsonga speech synthesis system that is as far as can be possible reasonably intelligible, natural sounding, and flexible. The system built has to be able to model some of the desirable speaker characteristics and speaking styles. This research project forms part of the broader national speech technology project that aims at developing spoken language systems for human-machine interaction using the eleven official languages of South Africa (SA). Speech synthesis is the reverse of automatic speech recognition (which receives speech as input and converts it to text) in that it receives text as input and produces synthesized speech as output. It is generally accepted that most people find listening to spoken utterances better that reading the equivalent of such utterances. The Xitsonga speech synthesis system has been developed using a hidden Markov model (HMM) speech synthesis method. The HMM-based speech synthesis (HTS) system synthesizes speech that is intelligible, and natural sounding. This method can synthesize speech on a footprint of only a few megabytes of training speech data. The HTS toolkit is applied as a patch to the HTK toolkit which is a hidden Markov model toolkit primarily designed for use in speech recognition to build and manipulate hidden Markov models.
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Yoon, Kyuchul. "Building a prosodically sensitive diphone database for a Korean text-to-speech synthesis system." Connect to this title online, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1119010941.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xxii, 291 p.; also includes graphics (some col.) Includes bibliographical references (p. 210-216). Available online via OhioLINK's ETD Center
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Beněk, Tomáš. "Implementing and Improving a Speech Synthesis System." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236079.

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Tato práce se zabývá syntézou řeči z textu. V práci je podán základní teoretický úvod do syntézy řeči z textu. Práce je postavena na MARY TTS systému, který umožňuje využít existujících modulů k vytvoření vlastního systému pro syntézu řeči z textu, a syntéze řeči pomocí skrytých Markovových modelů natrénovaných na vytvořené řečové databázi. Bylo vytvořeno několik jednoduchých programů ulehčujících vytvoření databáze a přidání nového jazyka a hlasu pro MARY TTS systém bylo demonstrováno. Byl vytvořen a publikován modul a hlas pro Český jazyk. Byl popsán a implementován algoritmus pro přepis grafémů na fonémy.
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Malatji, Promise Tshepiso. "The development of accented English synthetic voices." Thesis, University of Limpopo, 2019. http://hdl.handle.net/10386/2917.

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Thesis (M. Sc. (Computer Science)) --University of Limpopo, 2019<br>A Text-to-speech (TTS) synthesis system is a software system that receives text as input and produces speech as output. A TTS synthesis system can be used for, amongst others, language learning, and reading out text for people living with different disabilities, i.e., physically challenged, visually impaired, etc., by native and non-native speakers of the target language. Most people relate easily to a second language spoken by a non-native speaker they share a native language with. Most online English TTS synthesis systems are usually developed using native speakers of English. This research study focuses on developing accented English synthetic voices as spoken by non-native speakers in the Limpopo province of South Africa. The Modular Architecture for Research on speech sYnthesis (MARY) TTS engine is used in developing the synthetic voices. The Hidden Markov Model (HMM) method was used to train the synthetic voices. Secondary training text corpus is used to develop the training speech corpus by recording six speakers reading the text corpus. The quality of developed synthetic voices is measured in terms of their intelligibility, similarity and naturalness using a listening test. The results in the research study are classified based on evaluators’ occupation and gender and the overall results. The subjective listening test indicates that the developed synthetic voices have a high level of acceptance in terms of similarity and intelligibility. A speech analysis software is used to compare the recorded synthesised speech and the human recordings. There is no significant difference in the voice pitch of the speakers and the synthetic voices except for one synthetic voice.
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Cohen, Andrew Dight. "The use of learnable phonetic representations in connectionist text-to-speech system." Thesis, University of Reading, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360787.

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Breitenbücher, Mark. "Textvorverarbeitung zur deutschen Version des Festival Text-to-Speech Synthese Systems." [S.l.] : Universität Stuttgart , Fakultät Philosophie, 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB6783514.

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Lambert, Tanya. "Databases for concatenative text-to-speech synthesis systems : unit selection and knowledge-based approach." Thesis, University of East Anglia, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421192.

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Xiao, He. "An affective personality for an embodied conversational agent." Curtin University of Technology, Department of Computer Engineering, 2006. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=16139.

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Curtin Universitys Embodied Conversational Agents (ECA) combine an MPEG-4 compliant Facial Animation Engine (FAE), a Text To Emotional Speech Synthesiser (TTES), and a multi-modal Dialogue Manager (DM), that accesses a Knowledge Base (KB) and outputs Virtual Human Markup Language (VHML) text which drives the TTES and FAE. A user enters a question and an animated ECA responds with a believable and affective voice and actions. However, this response to the user is normally marked up in VHML by the KB developer to produce the required facial gestures and emotional display. A real person does not react by fixed rules but on personality, beliefs, previous experiences, and training. This thesis details the design, implementation and pilot study evaluation of an Affective Personality Model for an ECA. The thesis discusses the Email Agent system that informs a user when they have email. The system, built in Curtins ECA environment, has personality traits of Friendliness, Extraversion and Neuroticism. A small group of participants evaluated the Email Agent system to determine the effectiveness of the implemented personality system. An analysis of the qualitative and quantitative results from questionnaires is presented.
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XIE, GING-JIANG, and 謝清江. "A Chinese text-to-speech system based on formant synthesis." Thesis, 1987. http://ndltd.ncl.edu.tw/handle/68840754016731337307.

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Books on the topic "Text-to-speech synthesis system"

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Allen, Jonathan. From text to speech: The MITalk system. Cambridge University Press, 1987.

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Taylor, Paul. Text-to-speech synthesis. Cambridge University Press, 2009.

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An introduction to text-to-speech synthesis. Kluwer Academic Publishers, 1997.

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Sabourin, Conrad. Computational speech processing: Speech analysis, recognition, understanding, compression, transmission, coding, synthesis, text to speech systems, speech to tactile displays, speaker identification, prosody processing : bibliography. Infolingua, 1994.

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Dutoit, Thierry, and Yannis Stylianou. Text-to-Speech Synthesis. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0017.

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This article gives an introduction to state-of-the-art text-to-speech (TTS) synthesis systems, showing both the natural language processing and the digital signal processing problems involved. Text-to-speech (TTS) synthesis is the art of designing talking machines. The article begins with brief user-oriented description of a general TTS system and comments on its commercial applications. It then gives a functional diagram of a modern TTS system, highlighting its components. It describes its morphosyntactic module. Furthermore, it examines why sentence-level phonetization cannot be achieved by a sequence of dictionary look-ups, and describes possible implementations of the phonetizer. Finally, the article describes prosody generation, outlining how intonation and duration can approximately be computed from text. Prosody refers to certain properties of the speech signal, which are related to audible changes in pitch, loudness, and syllable length. This article also introduces the two main existing categories of techniques for waveform generation: synthesis by rule and concatenative synthesis.
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William, Sproat Richard, and Lucent Technologies (Firm), eds. Multilingual text-to-speech synthesis: The Bell Labs approach. Kluwer, 1998.

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van, Heuven Vincent, and Pols, Louis C. W., 1941-, eds. Analysis and synthesis of speech: Strategic research towards high-quality text-to-speech generation. Mouton de Gruyter, 1993.

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Book chapters on the topic "Text-to-speech synthesis system"

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Matoušek, Jindřich, and Daniel Tihelka. "Slovak Text-to-Speech Synthesis in ARTIC System." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30120-2_20.

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Jůzová, Markéta, and Daniel Tihelka. "Tuning Limited Domain Speech Synthesis Using General Text-to-Speech System." In Text, Speech and Dialogue. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10816-2_49.

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Singh, Parminder, and Gurpreet Singh Lehal. "Text-To-Speech Synthesis System for Punjabi Language." In Information Systems for Indian Languages. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19403-0_54.

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Ferri, Giuliano, Piero Pierucci, and Donatella Sanzone. "A Complete Linguistic Analysis for an Italian Text-to-Speech System." In Progress in Speech Synthesis. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1894-4_10.

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Shih, Chilin, and Benjamin Ao. "Duration Study for the Bell Laboratories Mandarin Text-to-Speech System." In Progress in Speech Synthesis. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1894-4_31.

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Abe, Masanobu. "Speaking Styles: Statistical Analysis and Synthesis by a Text-to-Speech System." In Progress in Speech Synthesis. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1894-4_39.

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James, Jesin, Isabella Shields, Rebekah Berriman, Peter J. Keegan, and Catherine I. Watson. "Developing Resources for Te Reo Māori Text To Speech Synthesis System." In Text, Speech, and Dialogue. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58323-1_32.

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Yoon, Kyuchul. "A Prosodic Diphone Database for Korean Text-to-Speech Synthesis System." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_45.

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Begum, Afruza, S. Md S. Askari, and Utpal Sharma. "Text-to-Speech Synthesis System for Mymensinghiya Dialect of Bangla Language." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0224-4_27.

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Sudhakar, B., and R. Bensraj. "Development of Concatenative Syllable-Based Text to Speech Synthesis System for Tamil." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2126-5_63.

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Conference papers on the topic "Text-to-speech synthesis system"

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Sharma, Bidisha, Nagaraj Adiga, and S. R. Mahadeva Prasanna. "Development of Assamese Text-to-speech synthesis system." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7372786.

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Mahanta, Deepshikha, Bidisha Sharma, Priyankoo Sarmah, and S. R. Mahadeva Prasanna. "Text to speech synthesis system in Indian English." In 2016 IEEE Region 10 Conference (TENCON). IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848511.

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Mahar, Javed Ahmed, Ghulam Qadir Memon, and Syed Hyder Abbass Shah. "WordNet Based Sindhi Text to Speech Synthesis System." In 2010 Second International Conference on Computer Research and Development. IEEE, 2010. http://dx.doi.org/10.1109/iccrd.2010.31.

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Anto, Ancy, and K. K. Nisha. "Text to speech synthesis system for English to Malayalam translation." In 2016 International Conference on Emerging Technological Trends (ICETT). IEEE, 2016. http://dx.doi.org/10.1109/icett.2016.7873642.

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Jalin, A. Femina, and J. Jayakumari. "Text to speech synthesis system for tamil using HMM." In 2017 IEEE International Conference on Circuits and Systems (ICCS). IEEE, 2017. http://dx.doi.org/10.1109/iccs1.2017.8326040.

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Nemala, Sridhar Krishna, Partha Pratim Talukdar, Kalika Bali, and A. G. Ramakrishnan. "Duration modeling for hindi text-to-speech synthesis system." In Interspeech 2004. ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-297.

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Watts, Oliver, and Bowen Zhou. "Unsupervised features from text for speech synthesis in a speech-to-speech translation system." In Interspeech 2011. ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-564.

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Kadhi, Aymen El, Fadhila Gherri, and Hamid Amiri. "Building diphone database for Arabic text to speech synthesis system." In 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT). IEEE, 2015. http://dx.doi.org/10.1109/ceit.2015.7233151.

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Wang, Lijuan, Xiaojun Qian, Lei Ma, Yao Qian, Yining Chen, and Frank K. Soong. "A real-time text to audio-visual speech synthesis system." In Interspeech 2008. ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-596.

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Mullani, J. J., M. Sankar, Priyanka S. Khade, Snehal H. Sonalkar, and Nikita L. Patil. "OCR BASED SPEECH SYNTHESIS SYSTEM USING LABVIEW : Text to Speech Conversion System using OCR." In 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018. http://dx.doi.org/10.1109/iccmc.2018.8487731.

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