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.
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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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
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