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Emotional Text-to-Speech (E-TTS) synthesis has garnered significant attention in recent years due to its potential to revolutionize human-computer interaction.
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J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” in International Conference on Machine Learning . PMLR, 2021, pp. 5530–5540
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C. Miao, L. Shuang, Z. Liu, C. Minchuan, J. Ma, S. Wang, and J. Xiao, “Efficienttts: An efficient and high-quality text-to-speech architecture,” in International Conference on Machine Learning . PMLR, 2021, pp. 7700–7709
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T. Li, S. Yang, L. Xue, and L. Xie, “Controllable emotion transfer for end-to-end speech synthesis,” in 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP) , Jan 2021, pp. 1–5
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Z. Guo, Y. Leng, Y. Wu, S. Zhao, and X. Tan, “Prompttts: Controllable text-to-speech with text descriptions,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
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X. Zhu, Y. Lei, T. Li, Y. Zhang, H. Zhou, H. Lu, and L. Xie, “Metts: Multilingual emotional text-to-speech by cross-speaker and cross-lingual emotion transfer,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 32, pp. 1506–1518, 2024
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2024
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2024
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W. Guan, Y. Li, T. Li, H. Huang, F. Wang, J. Lin, L. Huang, L. Li, and Q. Hong, “Mm-tts: Multi-modal prompt based style transfer for expressive text-to-speech synthesis,” in AAAI , 2024
2024
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J. Sternkopf and S. Taubert, “mel-cepstral-distance,” Jan. 2024. [Online]. Available: https://github.com/jasminsternkopf/mel_cepstral_distance
2024
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