MultiSpeech: Multi-Speaker Text to Speech with Transformer

Attention Is All You Need

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

MizAR 60 for Mizar 50

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

Layer NormalizationPreprint

Ba, J., Kiros, J., & Hinton, G. E. (2016) · arXiv.org · doi:10.48550/arxiv.1607.06450 · Cited by 13,745 · see citing works

“Librispeech: an ASR corpus based on public domain audio books,”

Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072 · 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

Natural 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

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

Tacotron: towards end-to-end speech synthesis

Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S., et al. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1452 · Cited by 1,746 · see citing works

Neuromodulatory Control Networks (NCNs): A Biologically Inspired Architecture for Dynamic LLM Processing

Morgan, M. C. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17575540 · Cited by 1,375 · see citing works

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Ye, J., Chen, Z., & Wu, Y. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2441 · Cited by 828 · see citing works

Neural Speech Synthesis with Transformer Network

Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754 · see citing works

FastSpeech: Fast, Robust and Controllable Text to SpeechPreprint

Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., & Liu, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.09263 · Cited by 724 · see citing works

Transfer learning from speaker verification to multispeaker text-to-speech synthesis

Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I. L., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.04558 · Cited by 519 · see citing works

SUPERSEDED - CSTR VCTK Corpus: English Multi-speaker Corpus for CSTR Voice Cloning Toolkit

Veaux, C., Yamagishi, J., & MacDonald, K. (2016) · University of Edinburgh. The Centre for Speech Technology Research (CSTR) · doi:10.7488/ds/1495 · Cited by 434 · see citing works

Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with TacotronPreprint

Skerry-Ryan, R., Battenberg, E., Xiao, Y., Wang, Y., Stanton, D., Shor, J., Weiss, R. J., Clark, R., & Saurous, R. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.09047 · Cited by 348 · see citing works

Deep voice 3: scaling text-to-speech with convolutional sequence learning

Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., & Miller, J. (2017) · arXiv · doi:10.48550/arxiv.1710.07654 · Cited by 313 · see citing works

Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention

Tachibana, H., Uenoyama, K., & Aihara, S. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461829 · Cited by 277 · see citing works

FastSpeech: Fast, Robust and Controllable Text to Speech

Ren, Y. (2024) · TIB Data Manager · doi:10.57702/jd8hw0cw · Cited by 256 · see citing works

Hierarchical Generative Modeling for Controllable Speech SynthesisPreprint

Hsu, W.-N., Zhang, Y., Weiss, R. J., Zen, H., Wu, Y., Wang, Y., Cao, Y. J., Ye, J., Chen, Z., Shen, J., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07217 · Cited by 165 · 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

Forward Attention in Sequence- To-Sequence Acoustic Modeling for Speech Synthesis

Zhang, J., Ling, Z.-H., & Dai, L.-R. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462020 · Cited by 82 · see citing works

Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS

He, M., Deng, Y., & He, L. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1972 · Cited by 76 · see citing works

Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion

Sun, H., Tan, X., Gan, J.-W., Liu, H., Zhao, S., Qin, T., & Liu, T. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1208 · Cited by 51 · see citing works

LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

Zen, H., Dang, V., Clark, R., Zhang, Y., Weiss, R. J., Jia, Y., Chen, Z., & Wu, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.02882 · Cited by 23 · see citing works

Initial investigation of an encoder-decoder end-to-end TTS framework using marginalization of monotonic hard latent alignmentsPreprint

Yasuda, Y., Wang, X., & Yamagishi, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.11535 · Cited by 5 · see citing works

Multimodal learning with transformers: A survey

Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072 · see citing works

A survey on neural speech synthesis

Tan, X., Qin, T., Soong, F., & Liu, T.-Y. (2021) · arXiv · doi:10.48550/arxiv.2106.15561 · Cited by 216 · see citing works

Hi-Fi Multi-Speaker English TTS Dataset

Bakhturina, E., Lavrukhin, V., Ginsburg, B., & Zhang, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1599 · Cited by 82 · see citing works

AI-Synthesized Voice Detection Using Neural Vocoder Artifacts

Sun, C., Jia, S., Hou, S., & Lyu, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00097 · Cited by 81 · see citing works

Transformers in speech processing: Overcoming challenges and paving the future

Latif, S., Zaidi, S. A. M., Cuayáhuitl, H., Shamshad, F., Shoukat, M., Usama, M., & Qadir, J. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100768 · Cited by 68 · see citing works

StyleTTS: A Style-Based Generative Model for Natural and Diverse Text-to-Speech Synthesis

Li, Y. A., Han, C., & Mesgarani, N. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3530171 · Cited by 64 · see citing works

Adaspeech 2: Adaptive Text to Speech with Untranscribed Data

Yan, Y., Tan, X., Li, B., Qin, T., Zhao, S., Shen, Y., & Liu, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414872 · Cited by 45 · 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

ESPnet2-TTS: Extending the Edge of TTS ResearchPreprint

Hayashi, T., Yamamoto, R., Yoshimura, T., Wu, P., Shi, J., Saeki, T., Ju, Y., Yasuda, Y., Takamichi, S., & Watanabe, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07840 · Cited by 35 · see citing works

Grad-StyleSpeech: Any-Speaker Adaptive Text-to-Speech Synthesis with Diffusion Models

Kang, M., Min, D., & Hwang, S. J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095515 · Cited by 34 · see citing works

WSRGlow: A Glow-Based Waveform Generative Model for Audio Super-Resolution

Zhang, K., Ren, Y., Xu, C., & Zhao, Z. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-892 · Cited by 33 · see citing works

EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model

Cui, C., Ren, Y., Liu, J., Chen, F., Huang, R., Lei, M., & Zhao, Z. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1148 · Cited by 32 · see citing works

DualLip

Chen, W., Tan, X., Xia, Y., Qin, T., Wang, Y., & Liu, T.-Y. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413623 · Cited by 28 · see citing works

GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis

Yang, J., Bae, J.-S., Bak, T., Kim, Y.-I., & Cho, H. Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-971 · Cited by 27 · see citing works

A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis

Jain, R., Yiwere, M. Y., Bigioi, D., Corcoran, P., & Cucu, H. (2022) · IEEE Access · doi:10.1109/access.2022.3170836 · Cited by 26 · see citing works

Controllable Accented Text-to-Speech Synthesis With Fine and Coarse-Grained Intensity Rendering

Liu, R., Şişman, B., Gao, G., & Li, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3378110 · Cited by 25 · see citing works

Accented Text-to-Speech Synthesis With Limited Data

Zhou, X., Zhang, M., Zhou, Y., Wu, Z., & Li, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3363414 · Cited by 24 · see citing works

Multilingual Byte2Speech Models for Scalable Low-resource Speech SynthesisPreprint

He, M., Yang, J., He, L., & Soong, F. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03541 · Cited by 24 · see citing works

Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker Adaptation in Text-to-Speech Synthesis

Zhou, Y., Song, C., Li, X., Zhang, L., Wu, Z., Bian, Y., Su, D., & Meng, H. M. L. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10054 · Cited by 21 · see citing works

MIST-Tacotron: End-to-End Emotional Speech Synthesis Using Mel-Spectrogram Image Style Transfer

Moon, S., Kim, S., & Choi, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3156093 · Cited by 21 · see citing works

Neural Dubber: Dubbing for Videos According to ScriptsPreprint

Hu, C., Tian, Q., Li, T., Wang, Y.-P., Wang, Y., & Zhao, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.08243 · Cited by 21 · see citing works

fairseq Sˆ2: A Scalable and Integrable Speech Synthesis Toolkit

Wang, C., Hsu, W.-N., Adi, Y., Polyak, A., Lee, A., Chen, P., Gu, J., & Pino, J. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.17 · Cited by 19 · see citing works

Speech Bert Embedding for Improving Prosody in Neural TTS

Chen, L., Deng, Y., Wang, X., Soong, F. K., & He, L. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413864 · Cited by 19 · see citing works

AraSpell: A Deep Learning Approach for Arabic Spelling CorrectionPreprint

Salhab, M., & Abu-Khzam, F. N. (2023) · Research Square · doi:10.21203/rs.3.rs-2974359/v1 · Cited by 18 · see citing works

MM-TTS: Multi-Modal Prompt Based Style Transfer for Expressive Text-to-Speech Synthesis

Guan, W., Li, Y., Li, T., Huang, H., Wang, F., Lin, J., Huang, L., Li, L., & Hong, Q. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i16.29769 · Cited by 17 · see citing works

Advancing Text-to-Speech Systems for Low-Resource Languages: Challenges, Innovations, and Future Directions

Bhushan, S., Mishra, V. P., Rishiwal, V., Sharmila, A., & Agarwal, U. (2025) · IEEE Access · doi:10.1109/access.2025.3605236 · Cited by 16 · see citing works

TDASS: Target Domain Adaptation Speech Synthesis Framework for Multi-speaker Low-Resource TTS

Zhang, X., Wang, J., Cheng, N., & Xiao, J. Z. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892596 · Cited by 14 · see citing works

A Machine Speech Chain Approach for Dynamically Adaptive Lombard TTS in Static and Dynamic Noise Environments

Novitasari, S., Sakti, S., & Nakamura, S. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3196879 · Cited by 12 · see citing works

Federated Learning with Dynamic Transformer for Text to Speech

Hong, Z., Wang, J., Qu, X., Liu, J., Zhao, C., & Xiao, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-2039 · Cited by 11 · see citing works

MuSE-SVS: Multi-Singer Emotional Singing Voice Synthesizer That Controls Emotional Intensity

Kim, S. Y., Kim, Y., Jun, J., & Kim, I. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3294712 · Cited by 11 · see citing works

Zero-Shot Normalization Driven Multi-Speaker Text to Speech Synthesis

Kumar, N., Narang, A., & Lall, B. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3169634 · Cited by 11 · see citing works

Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech

Choi, Y., Jung, Y., Suh, Y.-J., & Kim, H. (2022) · IEEE Access · doi:10.1109/access.2022.3175810 · Cited by 11 · see citing works

Investigations on speaker adaptation using a continuous vocoder within recurrent neural network based text-to-speech synthesis

Mandeel, A. R., Al-Radhi, M. S., & Csapó, T. G. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14005-5 · Cited by 11 · see citing works

SC-CNN: Effective Speaker Conditioning Method for Zero-Shot Multi-Speaker Text-to-Speech Systems

Yoon, H., Kim, C., Um, S., Yoon, H.-W., & Kang, H.-G. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3277786 · Cited by 11 · see citing works

CrossSpeech++: Cross-Lingual Speech Synthesis With Decoupled Language and Speaker Generation

Kim, J.-H., Yang, H.-S., Ju, Y.-C., Kim, I.-H., Kim, B.-Y., & Chung, J. S. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3547231 · Cited by 7 · see citing works

Text-to-Speech With Lip Synchronization Based on Speech-Assisted Text-to-Video Alignment and Masked Unit Prediction

Ahn, Y., Chae, J.-W., & Shin, J. W. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3537949 · Cited by 7 · see citing works

Self supervised learning for robust voice cloning

Klapsas, K., Ellinas, N., Nikitaras, K., Vamvoukakis, G., Kakoulidis, P., Μαρκόπουλος, Κ., Raptis, S., Sung, J. S., Jho, G., Chalamandaris, A., et al. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10856 · Cited by 6 · see citing works

Generating the Voice of the Interactive Virtual Assistant

Stan, A., & Lőrincz, B. (2021) · IntechOpen eBooks · doi:10.5772/intechopen.95510 · Cited by 6 · see citing works

Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis

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

Low-Resource Speech Synthesis with Speaker-Aware Embedding

Yang, L.-J., Yeh, I.-P., & Chien, J. (2022) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp57327.2022.10038221 · Cited by 5 · see citing works

VITS: Quality Vs. Speed Analysis

Matoušek, J., & Tihelka, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-40498-6_19 · Cited by 5 · see citing works

An objective evaluation of the effects of recording conditions and speaker characteristics in multi-speaker deep neural speech synthesis

Lőrincz, B., Stan, A., & Giurgiu, M. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.08.078 · Cited by 5 · see citing works

VoiceX as a Design Tool for Virtual Agents' Voices

Don, D. S. W., Lingenfelser, F., Kuch, J. M., Grothe, O., Schlagowski, R., André, E., & Mertes, S. (2025) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3717511.3749296 · Cited by 4 · see citing works

Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music Scores

Tang, J., Cooper, E., Wang, X., Yamagishi, J., & Fazekas, G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890623 · Cited by 4 · see citing works

Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis

Lőrincz, B., Stan, A., & Giurgiu, M. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616305 · Cited by 4 · see citing works

Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis

Zhou, X., Zhang, M., Zhou, Y., Wu, Z., & Li, H. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3657306 · Cited by 3 · see citing works

Acoustic Models

Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_5 · Cited by 3 · see citing works

Data Augmentation based Cross-Lingual Multi-Speaker TTS using DL with Sentiment Analysis

Lalitha, B., Madhurima, V., Ch, N., Babu, J. S., Sekhar, J. N. C., & Reddy, P. V. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3628428 · Cited by 3 · see citing works

Advanced Trans-BiGRU-QA Fusion Model for Atmospheric Mercury Prediction

Shih, D., Chung, F.-I., Wu, T.-W., Wang, B.-H., & Shih, M. (2024) · Mathematics · doi:10.3390/math12223547 · Cited by 3 · see citing works

MSDTRON: A High-Capability Multi-Speaker Speech Synthesis System for Diverse Data Using Characteristic Information

Wu, Q., Shen, Q., Luan, J., & Wang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747032 · Cited by 3 · see citing works

Meta-StyleSpeech: Multi-speaker adaptive text-to-speech generation

Min, D. (2025) · TIB Data Manager · doi:10.57702/fqzsmwin · Cited by 3 · see citing works

Dubbing Movies via Hierarchical Phoneme Modeling and Acoustic Diffusion Denoising

Li, L., Cong, G., Qi, Y., Zha, Z.-J., Wu, Q., Sheng, Q. Z., Huang, Q., & Yang, M. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3597267 · Cited by 2 · see citing works

Self-Adaptive Incremental Machine Speech Chain for Lombard TTS with High-Granularity ASR Feedback in Dynamic Noise Condition

Novitasari, S., Sakti, S., & Nakamura, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096128 · Cited by 2 · see citing works

ACCELERATION OF TRANSFORMER ARCHITECTURES ON JETSON XAVIER USING TENSORRT

Nikoghosyan, K. H., Khachatryan, T. B., Harutyunyan, E. A., & Galstyan, D. M. (2023) · doi:10.53297/18293336-2023.2-30 · Cited by 2 · see citing works

STUDENTS' DIFFICULTY IN USING PARTS OF SPEECH IN SPOOF TEXT IN STUDENTS OF ENGLISH LANGUAGE EDUCATION PROGRAM

Munthe, M. V. R., Sitanggang, A., Herman, H., & Mobo, F. D. (2023) · Dharmas Education Journal (DE_Journal) · doi:10.56667/dejournal.v4i2.1064 · Cited by 2 · see citing works

Facetron: A Multi-Speaker Face-to-Speech Model Based on Cross-Modal Latent Representations

Um, S., Kim, J., Lee, J., & Kang, H.-G. (2023) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco58844.2023.10290115 · Cited by 2 · see citing works

Advancing Limited Data Text-to-Speech Synthesis: Non-Autoregressive Transformer for High-Quality Parallel Synthesis

Al-Radhi, M. S., Ibrahim, O., Mandeel, A. R., Csapó, T. G., & Németh, G. (2023) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped59241.2023.10314948 · Cited by 2 · see citing works

Improving Language Model-Based Zero-Shot Text-to-Speech Synthesis with Multi-Scale Acoustic Prompts

Lei, S., Zhou, Y., Chen, L., Luo, D., Wu, Z., Wu, X., Kang, S., Jiang, T., Zhou, Y., Han, Y., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447815 · Cited by 2 · see citing works

FastSpeech2 Based Japanese Emotional Speech Synthesis

Ikeda, M., & Markov, K. (2024) · IEEE International Conference on Intelligent Systems · doi:10.1109/is61756.2024.10705252 · Cited by 2 · see citing works

DeepMine-multi-TTS: a Persian speech corpus for multi-speaker text-to-speech

Adibian, M., Zeinali, H., & Barmaki, S. (2025) · Language Resources and Evaluation · doi:10.1007/s10579-025-09807-6 · Cited by 2 · see citing works

End-to-End Multi-Speaker FastSpeech2 With Hierarchical Decoder

Adibian, M., & Zeinali, H. (2025) · IEEE Access · doi:10.1109/access.2025.3589120 · Cited by 2 · see citing works

Text Normalization for Indonesian Text-to-Speech (TTS) using Rule-Based Approach: A Dataset and Preliminary Study

Maghfur, N. M., Ibrohim, M. O., Fahmi, J., Putera, A. S., & Riandi, O. (2021) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie53219.2021.9649417 · Cited by 2 · see citing works

FCH-TTS: Fast, Controllable and High-quality Non-Autoregressive Text-to-Speech Synthesis

Zhou, X. Y., Zhou, Z., & Shi, X. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892512 · Cited by 2 · see citing works

Document-Level Neural TTS Using Curriculum Learning and Attention Masking

Hwang, S.-W., & Chang, J. (2021) · IEEE Access · doi:10.1109/access.2020.3049073 · Cited by 2 · see citing works

StoryForge: AI-Powered Narrative with Dynamic Imagery and Voice

Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Enathur, K., & DThirunavukkarasu, M. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem32973 · Cited by 1 · see citing works

Enhanced Prosody Modeling and Character Voice Controlling for Audiobook Speech Synthesis

Wu, N.-Q., & Ling, Z.-H. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3749644 · Cited by 1 · see citing works

TrInk: Ink Generation with Transformer Network

Jin, Z., Desai, S., Chen, X., Fang, B., Huang, Z., Li, Z., Gan, C.-X., Tu, X., Mak, M., Lu, Y., et al. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.244 · Cited by 1 · see citing works

RFGETT-TTS: Robust Fine-Grained Expressivity Transfer With Transformer for Text-to-Speech Synthesis

Hamed, M., Missaoui, I., & Lachiri, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3648672 · Cited by 1 · see citing works

Zero-Shot Voice Cloning Using Variational Embedding with Attention Mechanism

Lee, J., Kim, J., & Chang, J. (2021) · IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC) · doi:10.1109/ic-nidc54101.2021.9660599 · Cited by 1 · see citing works

A Contrastive Analysis Of Prefix In English And Batak

Sidabalok, F. Y., Purba, D. N., Sihombing, K., & Sitanggang, A. (2023) · PIJAR Jurnal Pendidikan dan Pengajaran · doi:10.58540/pijar.v1i3.381 · Cited by 1 · see citing works

Expressivity Transfer In Transformer-Based Text-To-Speech Synthesis

Hamed, M., & Lachiri, Z. (2024) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip62566.2024.10638975 · Cited by 1 · see citing works

Fine-Grained Prosody Transfer Text-to-Speech Synthesis with Transformer

Hamed, M., & Lachiri, Z. (2024) · International Conference in Electronic Engineering & Information Technology (EEITE) · doi:10.1109/eeite61750.2024.10654450 · Cited by 1 · see citing works

Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis

Ke, D., Deng, Y., Jia, Y., Xue, J., Luo, Q., Li, Y., Sun, J., Liang, J., & Lin, B. (2022) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp57327.2022.10037822 · Cited by 1 · see citing works

Speech BERT Embedding For Improving Prosody in Neural TTSPreprint

Chen, L., Deng, Y., Wang, X., Soong, F. K., & He, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04312

Effective and Differentiated Use of Control Information for Multi-speaker Speech Synthesis.Preprint

Wu, Q., Shen, Q., Luan, J., & Wang, Y. (2021) · arXiv (Cornell University)

Mispronunciation Detection and Correction via Discrete Acoustic Units.Preprint

Zhang, Z., Wang, Y., & Yang, J. (2021) · arXiv (Cornell University)

Perceptually Guided End-to-End Text-to-Speech With MOS PredictionPreprint

Choi, Y., Jung, Y., Suh, Y.-J., & Kim, H. (2020) · arXiv (Cornell University)

Model-Efficient TTS

Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_10

Expressive and Controllable TTS

Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_8

Robust TTS

Tan, X. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-99-0827-1_9

Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech

Yoon, H., Kim, C., Song, E., Yoon, H.-W., & Kang, H.-G. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1301

Generating Speech with Prosodic Prominence based on SSL-Visually Grounded Models

Hartanti, B. S. I., Tanaya, D., Azizah, K., Lestari, D. P., Purwarianti, A., & Sakti, S. (2023) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda60357.2023.10482965

Bridging Facial Imagery and Vocal Reality: Stable Diffusion-Enhanced Voice Generation

Lin, Y., Liu, D., Xu, Y., Suo, H., & Li, M. (2024) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp63861.2024.10800185

Dynamic Speech Generation to Enhance Intelligibility in Noisy Environments

Simantiraki, O., Markaki, M., & Pantazis, Y. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887886

Briefing of Text-to-Speech Trends Toward Zero-Shot Multi-Speaker Synthesis

Song, D., & Lee, J. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930170

Facilitating Voice Cloning in Intelligent Voice Assistants for Improving Mental Health of Elderly People

Deshmukh, R., Bhangale, H., Khambe, H., Shinde, A., & Gurav, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2694-6_15

Zero-Voice: A Low-Resource Approach For Zero-shot Text-to-SpeechPreprint

Wang, Z., Hou, F., & Wang, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.6590685

Detecting Computer Network Attacks in Time Series Network Data Using Transformer-Based Models

Fath, N. M. N., Shiddiqi, A. M., Mogi, I. K. A., Rizki, A., Syaefudin, M. A., & Rahma, F. (2025) · International Conference on Information & Communication Technology and System (ICTS) · doi:10.1109/icts67612.2025.11369713

TRAGIC: An Advanced Transformer–GRU Fusion Model with Self-Attention for Monkeypox Mortality Forecasting

Shih, D., Chan, P.-Y., Shih, P.-Y., Chen, B.-R., & Shih, M. (2026) · Mathematics · doi:10.3390/math14101674

Fast speech style adaptation with adjustable prosody and variable duration

Hua, Z., Yu, L., Liu, C., Sun, D., & Xie, H. (2026) · JUSTC · doi:10.52396/justc-2023-0135

EmoSay: Artificial Intelligence-Driven Text-to-Emotional-Speech System for Affective Communication in Extended Reality

Adewale, S., Ubur, S. D., Chandrashekar, N. D., Emebo, O., & Gračanin, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31421-5_1

Multi-speaker recognition with AdaptoNet and hybrid optimization approach

Mohanty, A., & Cherukuri, R. C. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21837-y

Multi-channel transformer: A transformer-based model for multi-speaker speech recognition

Fadeeva, E., & Ershov, V. (2022) · St. Petersburg State Polytechnical University Journal · doi:10.18721/jcstcs.15406

SC-GlowTTS: An Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

Casanova, E., Shulby, C., Gölge, E., Müller, N. M., de Oliveira, F. S., Cândido, A., Soares, A. D. S., Aluísio, S. M., & Ponti, M. A. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1774 · Cited by 94 · see citing works

Neural Speech Synthesis with Transformer Network

Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754 · see citing works

GANSpeech: Adversarial Training for High-Fidelity Multi-Speaker Speech Synthesis

Yang, J., Bae, J.-S., Bak, T., Kim, Y.-I., & Cho, H. Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-971 · Cited by 27 · see citing works

Leveraging Speaker Embeddings from Speaker Verification for Controllable Multispeaker Text-to-Speech

Thakkar, N., Yagnik, S. B., & Sharma, T. (2025) · Utilitas Mathematica