Abstract
Abstract. This paper deals with the clustering task for Russian texts obtained using automatic speech recognition (ASR). The input for processing are recognition result for phone call recordings and manual text transcripts for these calls. We present a comparative analysis of clustering results for recognition texts and manual text transcripts, make an evaluation of how recognition quality affects clustering and explore approaches to increasing clustering quality by using stop words and Latent Semantic Indexing (LSI).
This work was partially financially supported by the Government of Russian Federation, Grant 074-U01.
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
Larson, M., Jones, G.J.F.: Spoken content retrieval: A survey of techniques and technologies. Foundations and Trends in Information Retrieval 5(4-5), 235–422 (2012) ISSN 1554-0669
Park, A., Glass, J.R.: Unsupervised pattern discovery in speech. IEEE Trans. Acoustics, Speech and Language Processing 8(1), 186–197 (2008)
Deerwester, S., et al.: Improving Information Retrieval with Latent Semantic Indexing. In: Proceedings of the 51st Annual Meeting of the American Society for Information Science, vol. 25, pp. 36–40 (1988)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B (1977)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Chernykh, G., Korenevsky, M., Levin, K., Ponomareva, I., Tomashenko, N.: Cross-Validation State Control in Acoustic Model Training of Automatic Speech Recognition System. Scientific and Technical Journal Priborostroenie 57(2), 23–28 (2014)
Kudashev, O., Kozlov, A.: The Diarization System for an Unknown Number of Speakers. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 340–344. Springer, Heidelberg (2013)
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Trans. Audio, Speech and Language Proc. 20(1), 30–42 (2012)
Pinto, D.: Analysis of narrow-domain short texts clustering. In: Research report for Diploma de Estudios Avanzados (DEA). Department of Information Systems and Computation, UPV (2007)
Pinto, D., Rosso, P., Jimenez, H.: A Self-Enriching Methodology for Clustering Narrow Domain Short Texts. Comput. J. 54(7), 1148–1165 (2011)
Manning, C., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press (2009)
Eissen, S.M.z., Stein, B.: Analysis of Clustering Algorithms for Web-based Search. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2002. LNCS (LNAI), vol. 2569, pp. 168–178. Springer, Heidelberg (2002)
Stein, B., zu Eissen, S.M., Wibbrock, F.: On Cluster Validity and the Information Need of Users. In: Hanza, M.H. (ed.) 3rd IASTED Int. Conference on Artificial Intelligence and Applications (AIA 2003), Benalmadena, Spain, pp. 216–221. ACTA Press, IASTED (2003) ISBN 0-88986-390-3
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Popova, S., Khodyrev, I., Ponomareva, I., Krivosheeva, T. (2014). Automatic Speech Recognition Texts Clustering. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2014. Lecture Notes in Computer Science(), vol 8655. Springer, Cham. https://doi.org/10.1007/978-3-319-10816-2_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-10816-2_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10815-5
Online ISBN: 978-3-319-10816-2
eBook Packages: Computer ScienceComputer Science (R0)