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- W98284419 abstract "This article describes a new unsupervised methodology to learn F0 classes using HMM models on a syllable basis. A F0 class is represented by a HMM with three emitting states. The clustering algorithm relies on an iterative gaussian splitting and EM retraining process. First, a single class is learnt on a training corpus (8000 syllables) and it is then divided by perturbing gaussian means of successive levels. At each step, the mean RMS error is evaluated on a validation corpus (3000 syllables). The algorithm stops automatically when the error becomes stable or increases. The syllabic structure of a sentence is the reference level we have taken for F0 modelling even if the methodology can be applied to other structures. Clustering quality is evaluated in terms of cross-validation using a mean of RMS errors between F0 contours on a test corpus and the estimated HMM trajectories. The results show a pretty good quality of the classes (mean RMS error around 4Hz). Index Terms: prosody, fundamental frequency, unsupervised classification, Hidden Markov Model" @default.
- W98284419 created "2016-06-24" @default.
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- W98284419 date "2007-08-27" @default.
- W98284419 modified "2023-09-23" @default.
- W98284419 title "Unsupervised HMM classification of F0 curves" @default.
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- W98284419 doi "https://doi.org/10.21437/interspeech.2007-223" @default.
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