EXEMPLAR SELECTION METHODS IN VOICE CONVERSION
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2017 IEEE. Exemplar-based methods for voice conversion often use a large number of randomly-selected exemplars to ensure good coverage. As a result, the factorization step can be costly. This paper presents two algorithms that can be used to construct compact sets of exemplars. The first algorithm uses a forward selection procedure to build the exemplar set sequentially, selecting exemplar pairs that minimize the joint reconstruction error on source and target frames. The second algorithm uses a backward elimination procedure to remove exemplars that contribute the least to the factorization. We evaluate both selection strategies on voice conversion tasks using the ARCTIC corpus. Our results using objective measures and subjective listening tests show that both strategies can significantly reduce the size of the exemplar set (five-fold, in our experiments) while achieving the same performance on voice conversion.
name of conference
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)