At the moment more advanced approaches to phonetic transcription are required for different speech technology tasks such as TTS or ASR. All subtle differences in phonetic characteristics of sound sequences inside the words and in the word boundaries need more accurate and variable transcription rules. Moreover, there is a need to take into account not only the normal rules of phonetic transcription. it is important to include the information about speech variability in regional and social dialects, popular speech and colloquial variants of the high frequency lexis. In this paper a reliable method for automatic phonetic transcription of Russian text is presented. The system is used for making not only an ideal transcription for the Russian text but also takes into account the complex processes of sound change and variation within the Russian standard pronunciation. Our transcribing system is reliable and could be used not only for the TTS systems but also in ASR tasks that require more flexible approach to phonetic transcription of the text.
Translated title of the contributionАвтоматическая фонетическая ранскрипция для русского языка: моделирование вариативности речи
Original languageEnglish
Title of host publicationSpeech and Computer
Subtitle of host publication19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings
PublisherSpringer Nature
Pages192-199
ISBN (Electronic)978-3-319-66429-3
ISBN (Print)978-3-319-66428-6
StatePublished - Aug 2017
Event19th International Conference on Speech and Computer - Hatfield, United Kingdom
Duration: 11 Sep 201715 Sep 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10458
ISSN (Print)0302-9743

Conference

Conference19th International Conference on Speech and Computer
Abbreviated titleSPECOM 2017
Country/TerritoryUnited Kingdom
CityHatfield
Period11/09/1715/09/17

    Scopus subject areas

  • Arts and Humanities(all)
  • Computer Science(all)

    Research areas

  • Automatic phonetic transcription, Russian, Phonetics, Speech processing, Speech transcription, Speech variability modeling

ID: 13721395