Abstract
Finding the most likely sequence of symbols given a sequence of observations is a classical pattern recognition problem. This problem is frequently approached by means of the Viterbi algorithm, which aims at finding the most likely sequence of states within a trellis given a sequence of observations. Viterbi algorithm is widely used within the automatic speech recognition (ASR) framework to find the expected sequence of words given the acoustic utterance in spite of providing a suboptimal result. Word-graphs (WGs) are also frequently provided as the ASR output as a means of obtaining alternative hypotheses, hopefully more accurate than the one provided by the Viterbi algorithm. The trouble is that WGs can grow up in a very computationally inefficient manner. The aim of this work is to fully describe a specific method, computationally affordable, for getting a WG given the input utterance. The paper focuses specifically on the underlying approaches and their influence on both the spatial cost and the performance.
Keywords
- Lattice
- word-graphs
- automatic speech recognition
This work has been partially funded by the Spanish Ministry of Science and Innovation under the Consolider Ingenio 2010 programme (MIPRCV CSD2007-00018) and SD-TEAM project (TIN2008-06856-C05-01); and by the Basque Government (under grant GIC10/158 IT375-10).
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References
Forney Jr., G.D.: The Viterbi Algorithm. Proc. of the IEEE 61, 268–278 (1973)
Hazen, T.J., Seneff, S., Polifroni, J.: Recognition confidence scoring and its use in speech understanding systems. Computer Speech & Language 16, 49–67 (2002)
Ferreiros, J., Segundo, R.S., Fernández, F., D’Haro, L., Sama, V., Barra, R., Mellén, P.: New word-level and sentence-level confidence scoring using graph theory calculus and its evaluation on speech understanding. In: Proc. Interspeech, pp. 3377–3380 (2005)
Blackwood, G.: Lattice Rescoring Methods for Statistical Machine Translation. PhD thesis, University of Cambridge (2010)
Jelinek, F.: Statistical Methods for Speech Recognition, 2nd edn. Language, Speech and Communication series. The MIT Press, Cambridge (1999)
Huang, X., Acero, A., Hon, H.: Spoken Language Processing: A guide to Theory, Algorithm, and System Development. Prentice Hall, Englewood Cliffs (2001)
Caseiro, D., Trancoso, I.: A specialized on-the-fly algorithm for lexicon and language model composition. IEEE TASLP 14, 1281–1291 (2006)
Benedí, J., Lleida, E., Varona, A., Castro, M., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proc. of LREC 2006, Genoa, Italy (2006)
Pérez, A., Torres, M.I., Casacuberta, F., Guijarrubia, V.: A Spanish-Basque weather forecast corpus for probabilistic speech translation. In: Proc. of the 5t SALTMIL, Genoa, Italy (2006)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)
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Justo, R., Pérez, A., Torres, M.I. (2011). Impact of the Approaches Involved on Word-Graph Derivation from the ASR System. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_83
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DOI: https://doi.org/10.1007/978-3-642-21257-4_83
Publisher Name: Springer, Berlin, Heidelberg
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