The paper introduces CORPRES - COrpus of Russian Professionally REad Speech developed at the Department of Phonetics, Saint Petersburg State University, as a result of a three-year project. The corpus includes samples of different speaking styles produced by 4 male and 4 female speakers. Six levels of annotation cover all phonetic and prosodic information about the recorded speech data, including labels for pitch marks, phonetic events, phonetic, orthographic and prosodic transcription. Precise phonetic transcription of the data provides an especially valuable resource for both research and development purposes. Overall corpus size is 60 hours of speech. The paper contains information about CORPRES design and annotation principles, and overall data description. Also, we discuss possible use of the corpus in phonetic research and speech technology as well as some findings on the Russian sound system obtained from the corpus data.

Original languageEnglish
Title of host publicationText, Speech and Dialogue - 13th International Conference, TSD 2010, Proceedings
Place of PublicationBerlin Heidelberg
PublisherSpringer Nature
Pages392-399
Number of pages8
ISBN (Print)3642157599, 9783642157592
DOIs
StatePublished - 2010
Event13th International Conference on Text, Speech and Dialogue, TSD 2010: 13th International Conference - Brno, Czech Republic
Duration: 6 Sep 201010 Sep 2010
Conference number: 13
https://www.tsdconference.org/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6231 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Text, Speech and Dialogue, TSD 2010
Abbreviated titleTSD 2010
Country/TerritoryCzech Republic
CityBrno
Period6/09/1010/09/10
Internet address

    Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

    Research areas

  • annotation, manual transcription, phonetic transcription, Phonetics, prosodic feature labelling, speech corpus, text-to-speech

ID: 4428711