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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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works
Error bounds for convolutional codes and an asymptotically optimum decoding algorithm
Viterbi, A. J. (1967) · IEEE Transactions on Information Theory · doi:10.1109/tit.1967.1054010 · Cited by 6,877 · see citing works
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works
Self-Attention Generative Adversarial Networks
Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2018) · arXiv · doi:10.48550/arxiv.1805.08318 · Cited by 3,353 · see citing works
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 1× · see citing works
“Previous NMT models are dominated by RNN-based (Bahdanau, Cho, and Bengio 2014) or CNN-based (e.g. ConvS2S (Gehring et al. 2017), ByteNet (Kalchbrenner et al. 2016)) neural networks.”
MethodNatural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791 · see citing works
Signal estimation from modified short-time Fourier transform
Griffin, D. W., & Lim, J. S. (1984) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1984.1164317 · Cited by 2,085 · see citing works
Attention-Based Models for Speech Recognition
Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.07503 · Cited by 2,021 · see citing works
Unit selection in a concatenative speech synthesis system using a large speech database
Hunt, A., & Black, A. (1996) · 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings · doi:10.1109/icassp.1996.541110 · Cited by 1,508 · see citing works
Statistical parametric speech synthesis
Zen, H., Tokuda, K., & Black, A. W. (2009) · Speech Communication · doi:10.1016/j.specom.2009.04.004 · Cited by 1,202 · see citing works
Improving Variational Inference with Inverse Autoregressive FlowPreprint
Kingma, D., Salimans, T., Józefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04934 · Cited by 1,112 · see citing works
Speech parameter generation algorithms for HMM-based speech synthesis
Tokuda, K., Yoshimura, T., Masuko, T., Kobayashi, T., & Kitamura, T. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2000.861820 · Cited by 1,004 · see citing works
Statistical parametric speech synthesis using deep neural networks
Zen, H., Senior, A., & Schuster, M. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639215 · Cited by 818 · see citing works
SampleRNN: An Unconditional End-to-End Neural Audio Generation ModelPreprint
Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A. C., & Bengio, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07837 · Cited by 414 · see citing works
Neural Machine Translation in Linear Time
Kalchbrenner, N., Espeholt, L., Simonyan, K., Oord, A. V. D., Graves, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1610.10099 · Cited by 414 · cited 1× · see citing works
“Previous NMT models are dominated by RNN-based (Bahdanau, Cho, and Bengio 2014) or CNN-based (e.g. ConvS2S (Gehring et al. 2017), ByteNet (Kalchbrenner et al. 2016)) neural networks.”
MethodSpeech Synthesis Based on Hidden Markov Models
Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2251852 · Cited by 408 · see citing works
Parallel WaveNet: Fast High-Fidelity Speech SynthesisPreprint
van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10433 · Cited by 384 · see citing works
Verhelst, W., & Roelands, M. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319366 · Cited by 376 · see citing works
ClariNet: Parallel Wave Generation in End-to-End Text-to-SpeechPreprint
Ping, W., Peng, K.-N., & Chen, J.-T. (2018) · International Conference on Learning Representations · doi:10.48550/arxiv.1807.07281 · Cited by 370 · see citing works
Char2Wav: End-to-End Speech Synthesis
Sotelo, J., Mehri, S., Kumar, K., Santos, J. F., Kastner, K., Courville, A. C., & Bengio, Y. (2017) · International Conference on Learning Representations · Cited by 306 · see citing works
Automatically clustering similar units for unit selection in speech synthesis
Black, A. W., & Taylor, P. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-219 · Cited by 286 · see citing works
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram PredictionsPreprint
Shen, J., Pang, R., Weiss, R. J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.05884 · Cited by 185 · see citing works
Diphone synthesis using an overlap-add technique for speech waveforms concatenation
Charpentier, F., & Stella, M. (2005) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1986.1168657 · Cited by 158 · see citing works
Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model.Preprint
Wang, Y., Skerry-Ryan, R., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Samy Bengio, A., et al. (2017) · arXiv (Cornell University) · Cited by 142 · see citing works
Tacotron: Towards End-to-End Speech SynthesisPreprint
Wang, Y., Skerry-Ryan, R., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Samy Bengio, A., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.10135 · Cited by 123 · see citing works
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese
Zhou, S., Dong, L., Xu, S., & Xu, B. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1107 · Cited by 112 · see citing works
VoiceLoop: Voice Fitting and Synthesis via a Phonological LoopPreprint
Taigman, Y., Wolf, L., Polyak, A., & Nachmani, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06588 · Cited by 68 · see citing works
Zhou, S., Dong, L., Xu, S., & Xu, B. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04221-9_19 · Cited by 63 · see citing works
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChinesePreprint
Zhou, S., Dong, L., Xu, S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.10752 · Cited by 28 · see citing works
Zhou, S., Dong, L., Xu, S., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.06239 · Cited by 12 · see citing works
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Lin, T., Wang, Y., Liu, X., & Qiu, X. (2022) · AI Open · doi:10.1016/j.aiopen.2022.10.001 · Cited by 1,422 · see citing works
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Yamamoto, R., Song, E., & Kim, J.-M. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053795 · Cited by 850 · see citing works
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Karita, S., Chen, N., Hayashi, T., Hori, T., Inaguma, H., Jiang, Z., Someki, M., Yalta, N., Yamamoto, R., Wang, X., et al. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003750 · Cited by 766 · see citing works
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Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., & Wei, Y. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19812-0_38 · Cited by 752 · see citing works
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Subakan, C., Ravanelli, M., Cornell, S., Bronzi, M., & Zhong, J. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413901 · Cited by 695 · see citing works
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He, K., Chen, G., Li, Z., Rekik, I., Yin, Z., Wen, J., Gao, Y., Wang, Q., Zhang, J., & Shen, D. G. (2022) · Intelligent Medicine · doi:10.1016/j.imed.2022.07.002 · Cited by 571 · see citing works
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Kim, J., Kong, J., & Son, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06103 · Cited by 389 · see citing works
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Wang, C., Chen, Y., Zhang, S., & Zhang, Q. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118128 · Cited by 386 · see citing works
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Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389 · see citing works
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Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., & Wang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3329173 · Cited by 355 · see citing works
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Parallel Tacotron: Non-Autoregressive and Controllable TTS
Elias, I., Zen, H., Shen, J., Zhang, Y., Ye, J., Weiss, R. J., & Wu, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414718 · Cited by 75 · see citing works
Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Elias, I., Zen, H., Shen, J., Zhang, Y., Ye, J., Skerry-Ryan, R., & Wu, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1461 · Cited by 74 · see citing works
Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis
Wang, Y., Wang, X., Zhu, P., Wu, J., Li, H., Xue, H., Zhang, Y., Xie, L., & Bi, M. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-48 · Cited by 74 · see citing works
FastPitch: Parallel Text-to-speech with Pitch PredictionPreprint
Łańcucki, A. (2020) · arXiv (Cornell University) · doi:10.17023/03nj-k027 · Cited by 74 · see citing works
Kaneko, T., Tanaka, K., Kameoka, H., & Seki, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746713 · Cited by 72 · see citing works
Shen, J., Ye, J., Chrzanowski, M., Zhang, Y., Elias, I., Zen, H., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.04301 · Cited by 71 · see citing works
NAUTILUS: A Versatile Voice Cloning System
Luong, H.-T., & Yamagishi, J. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3034994 · Cited by 69 · see citing works
Fine-Grained Robust Prosody Transfer for Single-Speaker Neural Text-To-Speech
Klimkov, V., Ronanki, S., Rohnke, J., & Thomas Drugman, A. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2571 · Cited by 68 · see citing works
Cai, X., Dai, D., Wu, Z., Li, X., Li, J., & Meng, H. M. L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413907 · Cited by 68 · see citing works
Transformer: A General Framework from Machine Translation to Others
Zhao, Y., Zhang, J., & Zong, C. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1393-5 · Cited by 71 · see citing works
Lightspeech: Lightweight and Fast Text to Speech with Neural Architecture Search
Luo, R., Tan, X., Wang, R., Qin, T., Li, J., Zhao, S., Chen, E., & Liu, T.-Y. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414403 · Cited by 67 · see citing works
Self-citationCopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech
Karlapati, S., Moinet, A., Joly, A., Klimkov, V., Sáez-Trigueros, D., & Thomas Drugman, A. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1251 · Cited by 65 · see citing works
Full-Circuit Implementation of Transformer Network Based on Memristor
Yang, C., Wang, X., & Zeng, Z. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3136355 · Cited by 66 · see citing works
Aligntts: Efficient Feed-Forward Text-to-Speech System Without Explicit Alignment
Zeng, Z., Wang, J., Cheng, N., Xia, T., & Xiao, J. Z. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054119 · Cited by 65 · see citing works
Xu, J., Tan, X., Ren, Y., Qin, T., Li, J., Zhao, S., & Liu, T. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403331 · Cited by 83 · see citing works
Self-citationA deep learning method for the prediction of 6-DoF ship motions in real conditions
Zhang, M., Taimuri, G., Zhang, J., & Hirdaris, S. (2023) · Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment · doi:10.1177/14750902231157852 · Cited by 73 · see citing works
PnG BERT: Augmented BERT on Phonemes and Graphemes for Neural TTS
Ye, J., Zen, H., Shen, J., Zhang, Y., & Wu, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1757 · Cited by 72 · see citing works
Semi-Supervised Neural Architecture SearchPreprint
Luo, R., Tan, X., Wang, R., Qin, T., Chen, E., & Liu, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.10389 · Cited by 64 · see citing works
Reversible Vision Transformers
Mangalam, K., Fan, H., Li, Y., Wu, C.-Y., Xiong, B., Feichtenhofer, C., & Malik, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01056 · Cited by 58 · see citing works
Fine-Grained Emotion Strength Transfer, Control and Prediction for Emotional Speech Synthesis
Lei, Y., Yang, S., & Xie, L. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383524 · Cited by 57 · see citing works
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Gao, S., Li, Z., Qi, H., Cheng, M., & Wang, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183829 · Cited by 57 · see citing works
A Study of Non-autoregressive Model for Sequence Generation
Ren, Y., Liu, J., Tan, X., Zhao, Z., Zhao, S., & Liu, T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.15 · Cited by 56 · see citing works
Self-citationSequence-to-Sequence Singing Synthesis Using the Feed-Forward Transformer
Blaauw, M., & Bonada, J. (2019) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp40776.2020.9053944 · Cited by 57 · see citing works
Transformer-based Map Matching Model with Limited Ground-Truth Data using Transfer-Learning Approach
Jin, Z.-X., Kim, J., Yeo, H., & Choi, S. (2021) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103668 · Cited by 40 · see citing works
Yamamoto, R., Song, E., & Kim, J.-M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.11480 · Cited by 50 · see citing works
SingGAN: Generative Adversarial Network For High-Fidelity Singing Voice Generation
Huang, R., Cui, C., Chen, F., Ren, Y., Liu, J., Zhao, Z., Huai, B., & Wang, Z. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547854 · Cited by 46 · see citing works
A comprehensive review on transformer network for natural and medical image analysis
Thirunavukarasu, R., & Kotei, E. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100648 · Cited by 45 · see citing works
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Huang, W.-C., Hayashi, T., Wu, Y.-C., Kameoka, H., & Toda, T. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3049336 · Cited by 42 · see citing works
JDI-T: Jointly Trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment
Lim, D., Jang, W., Gyeonghwan, O., Park, H., Kim, B., & Yoon, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2123 · Cited by 43 · see citing works
Lee, S., Yoon, H.-W., Noh, H.-R., Kim, J., & Lee, S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17559 · Cited by 5 · see citing works
JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech
Lim, D., Jung, S., & Kim, E. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10294 · Cited by 43 · see citing works
The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
Xie, Y., Lu, Y., Fu, R., Wen, Z., Wang, Z., Tao, J., Qi, X., Wang, X., Liu, Y., Cheng, H., et al. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3525966 · Cited by 51 · see citing works
Self-citationPortaSpeech: Portable and High-Quality Generative Text-to-SpeechPreprint
Ren, Y., Liu, J., & Zhao, Z. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.15166 · Cited by 43 · see citing works
Multi-View Self-Attention Based Transformer for Speaker Recognition
Wang, R., Ao, J., Zhou, L., Liu, S., Wei, Z., Ko, T., Li, Q., & Zhang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746639 · Cited by 42 · see citing works
Self-citationBian, Y., Chen, C., Kang, Y., & Pan, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02373 · Cited by 42 · see citing works
Liz-López, H., Keïta, M., Ahmed, A. T., Hadid, A., Huertas‐Tato, J., & Camacho, D. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102103 · Cited by 43 · see citing works
SafeEar: Content Privacy-Preserving Audio Deepfake Detection
Li, X., Li, K., Zheng, Y., Yan, C., Ji, X., & Xu, W. (2024) · Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3658644.3670285 · Cited by 39 · see citing works
Revisiting Over-Smoothness in Text to Speech
Ren, Y., Tan, X., Qin, T., Zhao, Z., & Liu, T. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.564 · Cited by 39 · see citing works
Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis
Yang, K., Markovic, D., Krenn, S., Agrawal, V., & Richard, A. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00805 · Cited by 39 · see citing works
METTS: Multilingual Emotional Text-to-Speech by Cross-Speaker and Cross-Lingual Emotion Transfer
Zhu, X., Lei, Y., Li, T., Zhang, Y., Zhou, H., Lu, H., & Xie, L. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3363444 · Cited by 38 · see citing works
A review on subjective and objective evaluation of synthetic speech
Cooper, E., Huang, W.-C., Tsao, Y., Wang, H., Toda, T., & Yamagishi, J. (2024) · Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · doi:10.1250/ast.e24.12 · Cited by 37 · see citing works
NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms
Luo, C. W., Zhao, P., Qiao, B., Wu, Y., Zhang, H., Wu, W., Lu, W., Dang, Y., Rajmohan, S., Lin, Q., et al. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449867 · Cited by 37 · see citing works
Liu, R., Hu, Y., Zuo, H., Luo, Z., Wang, L., & Gao, G. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3348762 · Cited by 35 · see citing works
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers
Laugier, L., Pavlopoulos, J., Sorensen, J. S., & Dixon, L. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.124 · Cited by 37 · see citing works
A CNN-transformer hybrid approach for decoding visual neural activity into text
Zhang, J., Li, C., Liu, G., Min, M., Wang, C., Li, J., Wang, Y., Yan, H., Zuo, Z., Huang, W., et al. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106586 · Cited by 36 · see citing works
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
Hu, T.-Y., Shrivastava, A., Tuzel, O., & Dhir, C. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054591 · Cited by 35 · see citing works
Fine-Grained Style Control In Transformer-Based Text-To-Speech Synthesis
Chen, L., & Rudnicky, A. I. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747747 · Cited by 35 · see citing works
- 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 · 2018: 1
- 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 › Medicine › Physiology › 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.
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