Accent conversion (AC) seeks to transform second-language (L2) utterances to appear as if produced with a native (L1) accent. In the acoustic domain, AC is difficult due to the complex interaction between linguistic content and voice quality. Alternatively, AC can be performed in the articulatory domain by building a mapping from L2 articulators to L2 acoustics, and then driving the model with L1 articulators. However, collecting articulatory data for each L2 learner is impractical. Here we propose an approach that avoids this expensive step. Our method builds a cross-speaker forward mapping (CSFM) to generate L2 acoustic observations directly from L1 articulatory trajectories. We evaluated the CSFM against a baseline articulatory synthesizer trained with L2 articulators. Subjective listening tests show that both methods perform comparably in terms of accent reduction and ability to preserve the voice quality of the L2 speaker, with only a small impact in acoustic quality.
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