Abstract
Dealing with spontaneous speech constitutes big challenge both for linguistics and engineers of speech technology. For read speech, prosody was assessed as an automatic decomposition for phonological phrases using supervised method (HMM) in earlier experiments. However, when trying to adapt this automatic approach for spontaneous speech, the clustering of phonological phrase types becomes problematic: it is unknown which types can be characteristic and hence worth modelling. The authors decided to carry out a more flexible, unsupervised learning to cluster the data in order to evaluate and analyse whether some typical “spontaneous” patterns become selectable in spontaneous speech based on this automatic approach. This paper presents a method for clustering the typical prosody patterns of spontaneous speech based on k-means clustering.
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Beke, A., Szaszák, G. (2012). Unsupervised Clustering of Prosodic Patterns in Spontaneous Speech. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_79
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DOI: https://doi.org/10.1007/978-3-642-32790-2_79
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