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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.
Переведенное названиеАвтоматическая фонетическая ранскрипция для русского языка: моделирование вариативности речи
Язык оригиналаанглийский
Название основной публикацииSpeech and Computer
Подзаголовок основной публикации19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings
ИздательSpringer Nature
Страницы192-199
ISBN (электронное издание)978-3-319-66429-3
ISBN (печатное издание)978-3-319-66428-6
СостояниеОпубликовано - авг 2017
Событие19th International Conference on Speech and Computer - Hatfield, Великобритания
Продолжительность: 11 сен 201715 сен 2017

Серия публикаций

НазваниеLecture Notes in Computer Science
ИздательSpringer
Том10458
ISSN (печатное издание)0302-9743

конференция

конференция19th International Conference on Speech and Computer
Сокращенное названиеSPECOM 2017
Страна/TерриторияВеликобритания
ГородHatfield
Период11/09/1715/09/17

    Предметные области Scopus

  • Гуманитарные науки и искусство (все)
  • Компьютерные науки (все)

ID: 13721395