Abstract
In this paper, we present an approach to Spoken Language Understanding (SLU) where we perform a combination of multiple hypotheses from several Automatic Speech Recognizers (ASRs) in order to reduce the impact of recognition errors in the SLU module. This combination is performed using a Grammatical Inference algorithm that provides a generalization of the input sentences by means of a weighted graph of words. We have also developed a specific SLU algorithm that is able to process these graphs of words according to a stochastic semantic modelling.The results show that the combinations of several hypotheses from the ASR module outperform the results obtained by taking just the 1-best transcription.
This work is partially supported by the Spanish MEC under contract TIN2014-54288-C4-3-R and FPU Grant AP2010-4193
Chapter PDF
Similar content being viewed by others
References
Bangalore, S., Bordel, G., Riccardi, G.: Computing consensus translation from multiple machine translation systems. In: ASRU, pp. 351–354 (2001)
Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., de Letona, I.L., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: LREC, pp. 1636–1639 (2006)
Bonneau-Maynard, H., Lefèvre, F.: Investigating stochastic speech understanding. In: IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 260–263 (2001)
Calvo, M., García, F., Hurtado, L.F., Jiménez, S., Sanchis, E.: Exploiting multiple hypotheses for multilingual spoken language understanding. In: CoNLL, pp. 193–201 (2013)
Fiscus, J.G.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). In: 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354 (1997)
Hahn, S., Dinarelli, M., Raymond, C., Lefèvre, F., Lehnen, P., De Mori, R., Moschitti, A., Ney, H., Riccardi, G.: Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Transactions on Audio, Speech, and Language Processing 6(99), 1569–1583 (2010)
Hakkani-Tür, D., Béchet, F., Riccardi, G., Tür, 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.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006)
Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23(21), 2947–2948 (2007)
Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. IJPRAI 16(3), 301–307 (2002)
Tür, G., Deoras, A., Hakkani-Tür, D.: Semantic parsing using word confusion networks with conditional random fields. In: INTERSPEECH (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Calvo, M., Hurtado, LF., García, F., Sanchis, E. (2015). Combining Several ASR Outputs in a Graph-Based SLU System. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-25751-8_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25750-1
Online ISBN: 978-3-319-25751-8
eBook Packages: Computer ScienceComputer Science (R0)