Skip to main content

First Insight into the Processing of the Language Consulting Center Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11096))

Abstract

In this paper, we describe the initial stages of the project “Access to a Linguistically Structured Database of Enquiries from the Language Consulting Center”. This project is attempting to provide an improved access to the large archives of mainly telephone conversations collected continuously by the Institute of the Czech Language. The main goal is to open up the unique Czech data acquired from the queries to the Language Consulting Center and to build the semi-automatic system that will facilitate searching and categorizing of these queries. For this purpose, the Automatic Speech Recognizer (ASR) and the language processing methods are being designed. The vocabulary used in such queries contains many unusual words unlike the common speech (e.g. linguistic terms). In order to train the ASR system, it is necessary to manually transcribe a large amount of speech data, identify the appropriate vocabulary, and obtain relevant text for language modeling purposes. In this paper, the proposed telephone system for recording the new data and the baseline speech recognition on these data is described. The first experiments with the topic detection on these data aimed at discovering what can be found in them and also how to preprocess them is also described.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    ufal.morphodita at https://pypi.python.org/pypi/ufal.morphodita.

References

  1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  2. Bryant, R., Madsen, L., Meggelen, J.V.: Asterisk: The Definitive Guide: The Future of Telephony Is Now, 4th edn. O’Reilly Media (2013)

    Google Scholar 

  3. Černocký, J., Pollák Petr, H.V.: Czech speechdat(e) database. ELRA-S0077, ELRA (2000)

    Google Scholar 

  4. Ircing, P., Müller, L.: Benefit of proper language processing for czech speech retrieval in the CL-SR Task at CLEF 2006. In: Peters, P. (ed.) CLEF 2006. LNCS, vol. 4730, pp. 759–765. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74999-8_95

    Chapter  Google Scholar 

  5. Ircing, P., Psutka, J., Vavruška, J.: What can and cannot be found in czech spontaneous speech using document-oriented IR methods — UWB at CLEF 2007 CL-SR track. In: Peters, C. (ed.) CLEF 2007. LNCS, vol. 5152, pp. 712–718. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85760-0_90

    Chapter  Google Scholar 

  6. Kanis, J., Müller, L.: Automatic lemmatizer construction with focus on OOV words lemmatization. In: Matoušek, V., Mautner, P., Pavelka, T. (eds.) TSD 2005. LNCS (LNAI), vol. 3658, pp. 132–139. Springer, Heidelberg (2005). https://doi.org/10.1007/11551874_17

    Chapter  Google Scholar 

  7. Kanis, J., Skorkovská, L.: Comparison of different lemmatization approaches through the means of information retrieval performance. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS (LNAI), vol. 6231, pp. 93–100. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15760-8_13

    Chapter  Google Scholar 

  8. Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychol. Rev. 104, 211–240 (1997)

    Article  Google Scholar 

  9. Lilleberg, J., Zhu, Y., Zhang, Y.: Support vector machines and Word2vec for text classification with semantic features. In: IEEE Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), Beijing, pp. 136–140 (2015)

    Google Scholar 

  10. MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Statistics, pp. 281–297. University of California Press, Berkeley (1967)

    Google Scholar 

  11. Maergner, P., Waibel, A., Lane, I.: Unsupervised vocabulary selection for real-time speech recognition of lectures. In: ICASSP, Kyoto, pp. 4417–4420 (2012)

    Google Scholar 

  12. Paatero, P., Tapper, U.: Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5(2), 111–126 (1994). https://doi.org/10.1002/env.3170050203

    Article  Google Scholar 

  13. Petr, P., Černocký Jan, H.V.: Telephone speech data collection for czech. ELRA-S0094, ELRA (1999)

    Google Scholar 

  14. Povey, D.: nnet architecture (2017). https://github.com/kaldi-asr/kaldi/tree/master/egs/wsj/s5/steps/nnet

  15. Psutka, J., Jan, Š., Psutka, J.V., Van, J., Lubo, Š., Ircing, P.: System for fast lexical and phonetic spoken term detection in a Czech cultural heritage archive. EURASIP J. Audio Speech Music Process 2011, 1–11 (2011). https://doi.org/10.1186/1687-4722-2011-10

    Article  Google Scholar 

  16. Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)

    Google Scholar 

  17. Straková, J., Straka, M., Hajič, J.: Open-source tools for morphology, lemmatization, POS tagging and named entity recognition. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 13–18 (2014)

    Google Scholar 

  18. Švec, J., Šmídl, L., Ircing, P.: Hierarchical discriminative model for spoken language understanding. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8322–8326. IEEE, Vancouver (2013)

    Google Scholar 

  19. Wang, Y., Zhou, Z., Jin, S., Liu, D., Lu, M.: Comparisons and selections of features and classifiers for short text classification. In: International Conference on Artificial Intelligence Applications and Technologies (AIAAT), vol. 261, pp. 1–7. IEEE, Hawaii (2017)

    Google Scholar 

  20. Zelinka, J., Vaněk, J., Müller, L.: Neural-network-based spectrum processing for speech recognition and speaker verification. In: Dediu, A.-H., Martín-Vide, C., Vicsi, K. (eds.) SLSP 2015. LNCS (LNAI), vol. 9449, pp. 288–299. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25789-1_27

    Chapter  Google Scholar 

Download references

Acknowledgements

This research was supported by the Ministry of Culture Czech Republic, project No. DG16P02B009. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zbyněk Zajíc .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zajíc, Z. et al. (2018). First Insight into the Processing of the Language Consulting Center Data. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_79

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99579-3_79

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99578-6

  • Online ISBN: 978-3-319-99579-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics