Abstract
The paper deals with the semantic entity detection (SED) in the ASR lattices obtained by recognizing the air traffic control dialogs. The presented method is intended for the use in an automatic training tool for air traffic controllers. The semantic entities are modeled using the expert-defined context-free grammars. We use a novel approach which allows processing of uncertain input in the form of weighted finite state transducer. The method was experimentally evaluated on the real data. We also compare two methods for utilization of the knowledge about the dialog environment in the SED process. The results show that the SED with the knowledge about target semantic entities improves the equal error rate from 24.7% to 17.1% in comparison to generic SED.
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Allauzen, C., Riley, M., Schalkwyk, J.: OpenFst: A general and efficient weighted finite-state transducer library. Implementation and Application of Automata 4783, 11–23 (2007)
Can, D., Saraclar, M.: Lattice Indexing for Spoken Term Detection. IEEE Transactions on Audio, Speech and Language Processing 19(8), 2338–2347 (2011)
Hakkani-Tür, D., Béchet, F., Riccardi, G., Tur, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20(4), 495–514 (2006)
He, Y., Young, S.: Hidden vector state model for hierarchical semantic parsing. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 268–271 (2003)
Henderson, N., Gašić, M., Thomson, B., Tsiakoulis, P., Yu, K., Young, S.: Discriminative Spoken Language Understanding Using Word Confusion Networks. In: 2012 IEEE Spoken Language Technology Workshop (SLT), pp. 176–181 (2012)
Jurčíček, F., Švec, J., Zahradil, J., Jelínek, L.: Use of negative examples in training the HVS semantic model. Text, Speech and Dialogue 4188, 605–612 (2006)
Mohri, M., Moreno, P., Weinstein, E.: Factor automata of automata and applications. Implementation and Application of Automata 4783, 168–179 (2007)
Mohri, M., Pereira, F.C.N., Riley, M.: Weighted finite-state transducers in speech recognition. Computer Speech & Language 16(1), 69–88 (2002)
Povey, D., Hannemann, M., Boulianne, G., Burget, L., Ghoshal, A., Janda, M., Karafiát, M., Kombrink, S., Motlíček, P., Qian, Y., Riedhammer, K., Veselý, K., Vu, N.T.: Generating Exact Lattices in the WFST Framework. In: IEEE International Conference on Acoustics Speech and Signal Processing, Kyoto, Japan, vol. 213850, pp. 4213–4216. IEEE, Kyoto (2012)
Pražák, A., Psutka, J.V., Hoidekr, J., Kanis, J., Müller, L., Psutka, J.: Automatic online subtitling of the Czech parliament meetings. Text, Speech and Dialogue 4188, 501–508 (2006)
Šmídl, L.: Air traffic control communication corpus. Published in LINDAT/CLARING repository, available under CC BY-NC-ND 3.0 (2012), http://hdl.handle.net/11858/00-097C-0000-0001-CCA1-0
Švec, J., Ircing, P., Šmídl, L.: Semantic entity detection from multiple ASR hypotheses within the WFST framework. In: ASRU 2013: IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 84–89. IEEE, Olomouc (December 2013)
Švec, J., Šmídl, L., Ircing, P.: Hierarchical Discriminative Model for Spoken Language Understanding. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 8322–8326. IEEE, Vancouver (2013)
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Švec, J., Šmídl, L. (2014). Semantic Entity Detection in the Spoken Air Traffic Control Data. In: Ronzhin, A., Potapova, R., Delic, V. (eds) Speech and Computer. SPECOM 2014. Lecture Notes in Computer Science(), vol 8773. Springer, Cham. https://doi.org/10.1007/978-3-319-11581-8_49
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DOI: https://doi.org/10.1007/978-3-319-11581-8_49
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