Towards Unsupervised Articulatory Resynthesis of German Utterances using EMA data

As part of ongoing research towards integrating an articulatory synthesizer into a text-to-speech (TTS) framework, a corpus of German utterances recorded with electromagnetic articulography (EMA) is resynthesized to provide training data for statistical models. The resynthesis is based on a measure of similarity between the original and resynthesized EMA trajectories, weighted by articulatory relevance. Preliminary results are discussed and future work outlined.