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Presentation #12
Session:Detection, Paralinguistics and Coding
Session Time:Wednesday, December 19, 13:30 - 15:30
Presentation Time:Wednesday, December 19, 13:30 - 15:30
Presentation: Poster
Topic: Multimodal processing:
Paper Title: IMPROVING GENERALIZATION OF VOCAL TRACT FEATURE RECONSTRUCTION: FROM AUGMENTED ACOUSTIC INVERSION TO ARTICULATORY FEATURE RECONSTRUCTION WITHOUT ARTICULATORY DATA
Authors: Rosanna Turrisi; Istituto Italiano di Tecnologia 
 Raffaele Tavarone; Istituto Italiano di Tecnologia 
 Leonardo Badino; Istituto Italiano di Tecnologia 
Abstract: We address the problem of reconstructing articulatory movements, given audio and/or phonetic labels. The scarce availability of multi-speaker articulatory data makes it difficult to learn a reconstruction that generalizes to new speakers and across datasets. We first consider the XRMB dataset where audio, articulatory measurements and phonetic transcriptions are available. We show that phonetic labels, used as input to deep recurrent neural networks that reconstruct articulatory features, are in general more helpful than acoustic features in both matched and mismatched training-testing conditions. In a second experiment, we test a novel approach that attempts to build articulatory features from prior articulatory information extracted from phonetic labels. Such approach recovers vocal tract movements directly from an acoustic-only dataset without using any articulatory measurement. Results show that articulatory features generated by this approach can correlate up to 0.59 Pearson’s product-moment correlation with measured articulatory features.