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.
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Calvo, M., Hurtado, L. F., García, F., & Sanchis, E. (2015). Combining several ASR outputs in a graph-based SLU system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9423, pp. 551–558). Springer Verlag. https://doi.org/10.1007/978-3-319-25751-8_66
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