Representation learning is important for solving sequence-to-sequence problems in natural language processing. transforms raw data into vector-form representations while preserving their features. However, with significantly different features leads to heterogeneity representations, which may increase the difficulty of convergence. We design a multi-filter encoder-decoder model resolve problem ...

Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability convert prosody. Nonetheless, without sufficient data, seq2seq VC can suffer from unstable training and mispronunciation problems in the converted speech, thus far practical. To tackle these shortcomings, we propose transfer knowledge other speech processing tasks where large-scale corpora easily a...