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Neural Speech Synthesis with Transformer Network
Open accessAbstract
Although end-to-end neural text-to-speech (TTS) methods (such as Tacotron2) are proposed and achieve state-of-theart performance, they still suffer from two problems: 1) low efficiency during training and inference; 2) hard to model long dependency using current recurrent neural networks (RNNs). Inspired by the success of Transformer network in neural machine translation (NMT), in this paper, we introduce and adapt the multi-head attention mechanism to replace the RNN structures and also the original attention mechanism in Tacotron2. With the help of multi-head self-attention, the hidden states in the encoder and decoder are constructed in parallel, which improves training efficiency. Meanwhile, any two inputs at different times are connected directly by a self-attention mechanism, which solves the long range dependency problem effectively. Using phoneme sequences as input, our Transformer TTS network generates mel spectrograms, followed by a WaveNet vocoder to output the final audio results. Experiments are conducted to test the efficiency and performance of our new network. For the efficiency, our Transformer TTS network can speed up the training about 4.25 times faster compared with Tacotron2. For the performance, rigorous human tests show that our proposed model achieves state-of-the-art performance (outperforms Tacotron2 with a gap of 0.048) and is very close to human quality (4.39 vs 4.44 in MOS).
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Autotts: End-to-End Text-to-Speech Synthesis Through Differentiable Duration Modeling
Nguyen, B., Cardinaux, F., & Uhlich, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095431 · Cited by 5
Can Knowledge of End-to-End Text-to-Speech Models Improve Neural Midi-to-Audio Synthesis Systems?
Shi, X., Cooper, E., Wang, X., Yamagishi, J., & Narayanan, S. S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095848 · Cited by 5
Li, J., Li, S., Chen, P., Zhang, L., Yi, M., Wu, Z., Meng, H. M. L., Tian, Q., Wang, Y., & Wang, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3331813 · Cited by 6
Generating the Voice of the Interactive Virtual Assistant
Stan, A., & Lőrincz, B. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95510 · Cited by 6
Vocoder-free End-to-End Voice Conversion with Transformer Network
Kim, J.-W., Jung, H.-Y., & Lee, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207653 · Cited by 5
OkwuGbé: End-to-End Speech Recognition for Fon and IgboPreprint
Dossou, B. F. P., & Emezue, C. C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07762 · Cited by 8
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTSPreprint
Ye, J., Zen, H., Shen, J., Zhang, Y., & Wu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.15060 · Cited by 5
Beliaev, S., & Ginsburg, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08189 · Cited by 5
Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis.Preprint
Zaïdi, J., Seuté, H., van Niekerk, B., & Carbonneau, M. (2021) · arXiv (Cornell University) · Cited by 5
A Survey on Audio Synthesis and Audio-Visual Multimodal ProcessingPreprint
Shi, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.00443 · Cited by 18
fairseq S^2: A Scalable and Integrable Speech Synthesis ToolkitPreprint
Wang, C., Hsu, W.-N., Adi, Y., Polyak, A., Lee, A., Chen, P., Gu, J., & Pino, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06912 · Cited by 5
Survey on Multimodal Transformers for RobotsPreprint
Miyazawa, K., & Nagai, T. (2023) · doi:10.36227/techrxiv.21993317 · Cited by 5
Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis
Lanzino, R., Fontana, F., Cinque, L., Scarcello, F., & Maki, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91907-7_4 · Cited by 8
Yang, D., Huang, R., Wang, Y., Guo, H., Chong, D., Liu, S., Wu, X., & Meng, H. M. L. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3574847 · Cited by 15
Self-citation
Metadata
- Authors
- Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Microsoft Research Asia (China) (CN)
- Microsoft (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 6706-6713
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2903739847
- Cited by
- 754
- References
- 31
- Citations by year
- 2026: 28 · 2025: 88 · 2024: 103 · 2023: 131 · 2022: 138 · 2021: 165 · 2020: 89 · 2019: 11
- FWCI
- 38.55
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61572113)
- Fundamental Research Funds for the Central Universities (ZYGX2016J084, ZYGX2016J195, ZYGX2015J155, 61572113)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- neural text-to-speech, transformer models, multi-head self-attention, Tacotron2, long-range dependencies, mel-spectrogram generation, training efficiency, phoneme sequences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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