Abstract
Spontaneous speech is rarely fluent due to human nature. And among other characteristics of spontaneous speech there are the speech variation and the presence of speech disfluencies such as hesitations, fillers, artefacts. Such elements are an obstacle for automatic speech processing as well as for its tran-scriptions processing. For automatic detection of these elements a corpus of spontaneous Russian speech was collected basing on a task methodology. Corpus was annotated taking into account such types of disfluencies as hesitations, repairs, sound lengthening, as well as artefacts. For hesitation and artefacts detection there were used such parameters as duration, energy, fundamental frequency, and other spectral characteristics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Podlesskaya, V.I., Kibrik, A.A.: Speech disfluencies and their reflection in discourse transcription. In: Proceedings of VII International Conference on Cognitive Modelling in Linguistics, Varna, Bulgaria, vol. 1, pp. 194–204 (2004)
Clark, H.H., Fox Tree, J.E.: Using uh and um in spontaneous speaking. Cognition 84, 73–111 (2002)
Verkhodanova, V.O., Karpov, A.A.: Speech disfluencies modeling in the automatic speech recognition systems. The Bulletin of University of Tomsk 363, 10–15 (2012) (in Rus.)
Kipyatkova, I., Karpov, A., Verkhodanova, V., Zelezny, M.: Analysis of Long-distance Word Dependencies and Pronunciation Variability at Conversational Russian Speech Recognition. In: Proceedings of Federated Conference on Computer Science and Information Systems, FedCSIS 2012, Wroclaw, Poland, pp. 719–725 (2012)
Masataka, G., Katunobu, I., Satoru, H.: A real-time filled pause detection system for spontaneous speech Recognition. In: Proceedings of the 6th European Conference on Speech Communication and Technology, Eurospeech 1999, Budapest, Hungary, pp. 227–230 (1999)
Veiga, A., Candeias, S., Lopes, C., Perdigão, F.: Characterization of hesitations using acoustic models. In: Proceedings of the 17th International Congress of Phonetic Sciences, ICPhS XVII, Hong Kong, China, pp. 2054–2057 (2011)
Liu, Y., Shriberg, E., Stolcke, A.: Automatic Disfluency Identication in Conversational Speech Multiple Knowledge Sources. In: Proceedings of the 8th European Conference on Speech Communication and Technology, Eurospeech 2003, Geneva, Switzerland, pp. 957–960 (2003)
Liu, Y., Shriberg, E., Stolcke, A., et al.: Enriching Speech Recognition with Automatic Detection of Sentence Boundaries and Disfluencies. IEEE Transactions on Audio, Speech and Language Processing 1(5), 1526–1540 (2006)
Lease, M., Johnson, M., Charniak, E.: Recognizing disfluencies in conversational speech. IEEE Transactions on Audio, Speech and Language Processing 14(5), 1566–1573 (2006)
Kaushik, M., Trinkle, M., Hashemi-Sakhtsari, A.: Automatic Detection and Removal of Disfluencies from Spontaneous Speech. In: Proceedings of the 13th Australasian International Conference on Speech Science and Technology (SST), Melbourne, Australia, pp. 98–101 (2010)
Snover, M., Dorr, B., Schwartz, R.: A lexically-driven algorithm for disfluency detection. In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL-Short 2004, Boston, Massachusetts, USA, pp. 157–160 (2004)
Liu, Y.: Structural Event Detection for Rich Transcription of Speech. PhD thesis, Purdue University and ICSI, Berkeley, 253 p. (2004)
Corpus “Czech Broadcast Conversation MDE Transcripts”. In: LDC, http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2009T20 (accessed May 5, 2013)
Corpus “Czech Broadcast Conversation Speech”. In: LDC, http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC2009S02 (accessed May 5, 2013)
Kol\(\acute{a}\breve{r}\), J., \(\breve{S}\)vec, J., Strassel, S., et al.: Czech Spontaneous Speech Corpus with Structural Metadata. In: Proceedings of the 9th European Conference on Speech Communication and Technology, INTERSPEECH 2005, Lisbon, Portugal, pp. 1165–1168 (2005)
Zemskaya, E.A.: Russian spoken speech: linguistic analysis and the problems of learning, Moscow (1979) (in Rus.)
Anderson, A., Bader, M., Bard, E., Boyle, E., Doherty, G.M., Garrod, S., Isard, S., Kowtko, J., McAllister, J., Miller, J., Sotillo, C., Thompson, H.S., Weinert, R.: The HCRC Map Task Corpus. Language and Speech 34, 351–366 (1991)
Kohler, K.J.: Labelled data bank of spoken standard German: the Kiel corpus of read/spontaneous speech. In: Proceedings of Fourth International Conference on Spoken Language, ICSLP 1996, vol. 3, pp. 1938–1941 (1996)
Wave Assistant, the speech analyzer program by Speech Technology Center, http://www.phonetics.pu.ru/wa/WA_S.EXE (accessed January 5, 2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Verkhodanova, V., Shapranov, V. (2013). Automatic Detection of Speech Disfluencies in the Spontaneous Russian Speech. In: Železný, M., Habernal, I., Ronzhin, A. (eds) Speech and Computer. SPECOM 2013. Lecture Notes in Computer Science(), vol 8113. Springer, Cham. https://doi.org/10.1007/978-3-319-01931-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-01931-4_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01930-7
Online ISBN: 978-3-319-01931-4
eBook Packages: Computer ScienceComputer Science (R0)