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The FBK ASR System for Evalita 2011

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7689))

Abstract

This report describes the system used in FBK for participating in the large vocabulary Automatic Speech Recognition tasks of the Evalita 2011 evaluation campaign. The paper provides some details on the techniques included in the transcription system. The official FBK submissions were only related to the closed modality, were only data distributed within the campaign could be exploited. In this paper, results are given that were obtained with a system trained on larger corpora, thus allowing to appreciate the difference between the two modalities.

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References

  1. Giuliani, D., Brugnara, F.: Experiments on cross-system acoustic model adaptation. In: Proceedings of Workshop on Automatic Speech Recognition and Understanding, Kyoto, Japan, pp. 117–120 (2007)

    Google Scholar 

  2. Cettolo, M.: Segmentation, Classification and Clustering of an Italian Broadcast News Corpus. In: Proceedings of RIAO, pp. 372–381 (2000)

    Google Scholar 

  3. Young, S.J., Odell, J.J., Woodland, P.C.: Tree-based state tying for high accuracy acoustic modelling. In: Proceedings of ARPA Human Language Technology Workshop, pp. 286–291 (1994)

    Google Scholar 

  4. Giuliani, D., Gerosa, M., Brugnara, F.: Improved automatic speech recognition through speaker normalization. Computer Speech and Language 20, 107–123 (2006)

    Article  Google Scholar 

  5. Gales, M.J.F.: Maximum likelihood linear transformations for HMM-based speech recognition. Computer Speech and Language 20, 75–98 (1998)

    Article  Google Scholar 

  6. Gales, M.J.F.: Adaptive training for robust ASR. In: Proceedings of Workshop on Automatic Speech Recognition and Understanding, pp. 15–20 (2001)

    Google Scholar 

  7. Kumar, N., Andreou, A.G.: Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition. Speech Communication 26, 283–297 (1998)

    Article  Google Scholar 

  8. Stemmer, G., Brugnara, F.: Integration of Heteroscedastic Linear Discriminant Analysis (HLDA) into Adapative Training. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse (2006)

    Google Scholar 

  9. Stemmer, G., Brugnara, F., Giuliani, D.: Adaptive training using simple target models. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 997–1000 (2005)

    Google Scholar 

  10. http://www.phon.ucl.ac.uk/home/sampa/italian.html

  11. Ney, H., Essen, U.: On smoothing techniques for bigram-based natural language modeling. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, Toronto, Canada, pp. 825–828 (1991)

    Google Scholar 

  12. Federico, M., Bertoldi, N., Cettolo, M.: IRSTLM: an Open Source Toolkit for Handling Large Scale Language Models. In: Proceedings of Interspeech, Brisbane, Australia (2008)

    Google Scholar 

  13. Ney, H., Haeb-Umbach, R., Tran, B.-H., Oerder, M.: Improvements in beam search for 10000-word continuous speech recognition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, S.Francisco, CA, vol. I, pp. 9–12 (1992)

    Google Scholar 

  14. Brugnara, F., Cettolo, M.: Improvements in Tree based Language Model Representation. In: Proceedings of EUROSPEECH, Madrid, Spain, pp. 1797–1800 (1995)

    Google Scholar 

  15. Brugnara, F.: Context-dependent Search in a Context-independent Network. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, Hong Kong, vol. 1, pp. 360–363 (2003)

    Google Scholar 

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Ronny, R., Shakoor, A., Brugnara, F., Gretter, R. (2013). The FBK ASR System for Evalita 2011. In: Magnini, B., Cutugno, F., Falcone, M., Pianta, E. (eds) Evaluation of Natural Language and Speech Tools for Italian. EVALITA 2012. Lecture Notes in Computer Science(), vol 7689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35828-9_32

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  • DOI: https://doi.org/10.1007/978-3-642-35828-9_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35827-2

  • Online ISBN: 978-3-642-35828-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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