Phoneme-guided Dysarthric speech conversion With non-parallel data by joint training
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-021-02119-6.pdf
Reference28 articles.
1. Aihara, R., Takashima, R., Takiguchi, T., Ariki, Y.: Individuality-preserving voice conversion for articulation disorders based on non-negative matrix factorization. In: IEEE International Conference on Acoustics Speech and Signal Proceedings (ICASSP), pp. 8037–8040 (2013)
2. Aihara, R., Takashima, R., Takiguchi, T., Ariki, Y.: A preliminary demonstration of exemplar-based voice conversion for articulation disorders using an individuality-preserving dictionary. EURASIP J. Audio, Speech Music Process. 2014(1), 1–10 (2014)
3. Aihara, R., Takiguchi, T., Ariki, Y.: Multiple non-negative matrix factorization for many-to-many voice conversion. IEEE/ACM Transitions on Audio Speech and Language Processing (TASLP) 24(7), 1175–1184 (2016)
4. Aihara, R., Takiguchi, T., Ariki, Y.: Phoneme-discriminative features for dysarthric speech conversion. In: Proceedings of Interspeech, pp. 3374–3378 (2017)
5. Chen, J., Takiguchi, T., Ariki, Y.: Rotation-reversal invariant hog cascade for facial expression recognition. Signal, Image Video Process. (SIVP) 11(8), 1485–1492 (2017)
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1. A Multidomain Generative Adversarial Network for Hoarse-to-Normal Voice Conversion;Journal of Voice;2023-10
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