Book ; Online: Semi-supervised voice conversion with amortized variational inference
2019
Abstract: In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker. The proposed method makes use of both parallel and non-parallel utterances from the ... ...
Abstract | In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker. The proposed method makes use of both parallel and non-parallel utterances from the source and target simultaneously during training. This approach can be used to extend existing parallel data voice conversion systems such that they can be trained with semi-supervision. We show that incorporating semi-supervision improves the voice conversion performance compared to fully supervised training when the number of parallel utterances is limited as in many practical applications. Additionally, we find that increasing the number non-parallel utterances used in training continues to improve performance when the amount of parallel training data is held constant. Comment: Accepted for publication at Interspeech 2019 |
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Keywords | Statistics - Machine Learning ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Audio and Speech Processing |
Subject code | 006 |
Publishing date | 2019-09-30 |
Publishing country | us |
Document type | Book ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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