Mogale, MMSefara, Tshephisho JMokgonyane, TB2020-08-242020-08-242020-09Mogale, M.M., Sefara, T.J. and Mokgonyane, T.B. 2020. Grammar-driven text-to-speech application for articulation of mathematical expressions. Southern Africa Telecommunication Networks and Applications Conference, Fairmont Zimbali Resort, Ballito, KwaZulu-Natal, South Africa, 1-4 September 2019, 6pp.https://www.researchgate.net/publication/336210814_Grammar-driven_Text-to-speech_Application_for_Articulation_of_Mathematical_Expressionshttps://sites.google.com/view/tumisho-mokgonyane/publicationshttp://hdl.handle.net/10204/11558Copyright: 2020 Southern Africa Telecommunication Networks and Applications Conference (SATNAC). This is the full text version of the work.Natural Language Processing (NLP) forms one of the important and fundamental components of speech synthesis while a language grammar forms one of the important requirements for NLP tasks. One of the major requirements in processing speech synthesis tasks is the correctness of grammar analysis. Grammar-based applications tend to be effective when embedded within text-to-speech (TTS) synthesis systems. The TTS synthesis systems assist with the correct word spelling and intonation. Spoken languages plays a vital role to the educational journey of children as their brains are naturally wired to speak but not read and write. This paper presents the development of a grammar-driven TTS application for the reading of mathematical expressions in the Sepedi language. The application front-end component parses mathematical expression text inputs before a TTS synthesis system processes them to produce the correct articulation of the mathematical expression. Acceptable performance results are observed when the application is evaluated using word error rate for intelligibility, and subjective mean opinion score for pronunciation, naturalness, pleasantness, understandability, and overall system impression. The application achieved an accuracy 84,85%.enGrammar parserSpeech synthesisLanguage learningConference PresentationMogale, M., Sefara, T. J., & Mokgonyane, T. (2020). Grammar-driven text-to-speech application for articulation of mathematical expressions. Southern Africa Telecommunication Networks and Applications Conference (SATNAC). http://hdl.handle.net/10204/11558Mogale, MM, Tshephisho J Sefara, and TB Mokgonyane. "Grammar-driven text-to-speech application for articulation of mathematical expressions." (2020): http://hdl.handle.net/10204/11558Mogale M, Sefara TJ, Mokgonyane T, Grammar-driven text-to-speech application for articulation of mathematical expressions; Southern Africa Telecommunication Networks and Applications Conference (SATNAC); 2020. http://hdl.handle.net/10204/11558 .TY - Conference Presentation AU - Mogale, MM AU - Sefara, Tshephisho J AU - Mokgonyane, TB AB - Natural Language Processing (NLP) forms one of the important and fundamental components of speech synthesis while a language grammar forms one of the important requirements for NLP tasks. One of the major requirements in processing speech synthesis tasks is the correctness of grammar analysis. Grammar-based applications tend to be effective when embedded within text-to-speech (TTS) synthesis systems. The TTS synthesis systems assist with the correct word spelling and intonation. Spoken languages plays a vital role to the educational journey of children as their brains are naturally wired to speak but not read and write. This paper presents the development of a grammar-driven TTS application for the reading of mathematical expressions in the Sepedi language. The application front-end component parses mathematical expression text inputs before a TTS synthesis system processes them to produce the correct articulation of the mathematical expression. Acceptable performance results are observed when the application is evaluated using word error rate for intelligibility, and subjective mean opinion score for pronunciation, naturalness, pleasantness, understandability, and overall system impression. The application achieved an accuracy 84,85%. DA - 2020-09 DB - ResearchSpace DP - CSIR KW - Grammar parser KW - Speech synthesis KW - Language learning LK - https://researchspace.csir.co.za PY - 2020 T1 - Grammar-driven text-to-speech application for articulation of mathematical expressions TI - Grammar-driven text-to-speech application for articulation of mathematical expressions UR - http://hdl.handle.net/10204/11558 ER -