Neural Speech Synthesis with Transformer Network

Attention Is All You Need

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Long Short-Term Memory

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MizAR 60 for Mizar 50

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Sequence to Sequence Learning with Neural Networks

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Neural Machine Translation by Jointly Learning to Align and Translate

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Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

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WaveNet: a generative model for raw audio

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Self-Attention Generative Adversarial Networks

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Convolutional Sequence to Sequence Learning

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Leveraging Low-Rank Adaptation for Parameter-Efficient Fine-Tuning in Multi-Speaker Adaptive Text-to-Speech Synthesis

Hong, C., Lee, J. H., & Kim, H. K. (2024) · IEEE Access · doi:10.1109/access.2024.3515206 · Cited by 2

Continual Learning in Machine Speech Chain Using Gradient Episodic Memory

Tyndall, G., Arizah, K., Tanaya, D., Purwarianti, A., Lestari, D. P., & Sakti, S. (2024) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda64382.2024.10800352 · Cited by 2

Emotion-Aware Prosodic Phrasing for Expressive Text-to-Speech

Liu, B., Liu, R., & Li, H. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-1045-7_27 · Cited by 2

SlimSpeech: Lightweight and Efficient Text-to-Speech with Slim Rectified Flow

Wang, K., Guan, W., Lu, S., Yao, J., Li, L., & Hong, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889258 · Cited by 2

Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation

Shen, M., Zhang, S., Wu, J., Xiu, Z., AlBadawy, E. A., Lu, Y., Seltzer, M., & He, Q. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890141 · Cited by 2

CMDF-TTS: Text-to-speech method with limited target speaker corpus

Tao, Y., Liu, J., Lü, C., Liu, M., Qin, X., Tian, Y., & Du, Y. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107432 · Cited by 3

FastTalker: Jointly Generating Speech and Conversational Gestures from Text

Guo, Z., & Zhang, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93806-1_14 · Cited by 5

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

Pretraining and Fine-Tuning Techniques for Electrolaryngeal Speech Enhancement Based on Sequence-to-Sequence Voice Conversion

Ma, D., Violeta, L. P., Kobayashi, K., & Toda, T. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3577374 · Cited by 2

End-to-end Speech Separation with Neural Networks

Luo, Y. (2021) · doi:10.7916/d8-2bsv-xt40 · Cited by 2

Story Visualization: Generation of Scenes sequentially for a given fable using NLP and Image Processing

Nanda, S. D., Gagana, G. S., Changela, H. L., & Kumar, N. S. (2021) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon51346.2021.9545002 · Cited by 2

A New Speech Encoder Based on Dynamic Framing Approach

Liu, R., Yang, J., Zhou, X., & Yue, X. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.021995 · Cited by 2

Visual-Aware Text-to-Speech*

Zhou, M., Bai, Y., Zhang, W., Yao, T., Zhao, T., & Mei, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095084 · Cited by 2

Language modeling and bidirectional coders representations: an overview of key technologies

Kachkou, D. I. (2021) · Informatics · doi:10.37661/1816-0301-2020-17-4-61-72 · Cited by 2

Synthesizing waveform sequence-to-sequence to augment training data for sequence-to-sequence speech recognition

Ueno, S., Mimura, M., Sakai, S., & Kawahara, T. (2021) · Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · doi:10.1250/ast.42.333 · Cited by 2

Construction and Analysis of Tibetan AMDO Dialect Speech Dataset for Speech Synthesis

Zhang, X., Lu, W., Zhao, X., Zhu, Y., & Wei, J. (2021) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda202152914.2021.9660562 · Cited by 2

Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations

Liu, C., Ling, Z.-H., & Chen, L.-H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10140 · Cited by 2

Monotonic Gaussian regularization of attention for robust automatic speech recognition

Du, Y., Wu, M., Fang, X., & Yang, Z. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101405 · Cited by 3

Fusing Acoustic and Text Emotional Features for Expressive Speech Synthesis

Feng, Y., Duan, P., Zi, Y., Chen, Y., & Xiong, S. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859769 · Cited by 2

Behavioral Data Categorization for Transformers-based Models in Digital Health

Siebra, C., Matias, I., & Wac, K. (2022) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi56158.2022.9926938 · Cited by 3

Enriching Style Transfer in multi-scale control based personalized end-to-end speech synthesis

Lyu, Z., & Zhu, J. B. (2022) · International Conference on Information Science and Technology · doi:10.1109/icist55546.2022.9926908 · Cited by 2

Research on synthesis of designated speaker speech based on StarGAN-VC model

Qiu, X., & Luo, Y. (2022) · International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP) · doi:10.1117/12.2659719 · Cited by 2

VStyclone: Real-time Chinese voice style clone

Wu, Y., Zhao, H., Liang, X., & Sun, Y. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108534 · Cited by 3

Do You Know How Humans Sound? Exploring a Qualification Test Design for Crowdsourced Evaluation of Voice Synthesis Quality

Yaegashi, M., Saito, S., Nakano, T., & Ogawa, T. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9979812 · Cited by 2

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

Towards Scalable Anomaly Detection for Embedded Devices through Synthetic EM Fingerprinting

Vedros, K. A., Makrakis, G. M., Kolias, C., Ivans, R. C., & Rieger, C. G. (2023) · International Conference on Computer Science, Engineering and Applications (CSEA) · doi:10.5121/csit.2023.130507 · Cited by 2

ASR Model Adaptation for Rare Words Using Synthetic Data Generated by Multiple Text-To-Speech Systems

Yuen, K. C., Li, H., & Siong, C. E. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317116 · Cited by 2

Direct Vs Cascaded Speech-to-Speech Translation Using Transformer

Arya, L., Chowdhury, A. R., & Prasanna, S. R. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48312-7_21 · Cited by 2

Chinese Speech Synthesis System Based on End to End

Zhang, H., & Huang, X. (2020) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess49938.2020.9237634 · Cited by 2

Challenges for Edge-AI Implementations of Text-To-Speech Synthesis

Bigioi, D., & Corcoran, P. (2021) · International Conference on Consumer Electronics · doi:10.1109/icce50685.2021.9427679 · Cited by 2

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech SynthesisPreprint

Hu, T.-Y., Shrivastava, A., Tuzel, O., & Dhir, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.06227 · Cited by 2

Efficient, end-to-end and self-supervised methods for speech processing and generation

Pascual, S. (2020) · doi:10.5821/dissertation-2117-180792 · Cited by 2

Parallel waveform synthesis based on generative adversarial networks with voicing-aware conditional discriminatorsPreprint

Yamamoto, R., Song, E., Hwang, M.-J., & Kim, J.-M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14151 · Cited by 2

MelGlow: Efficient Waveform Generative Network Based on Location-Variable ConvolutionPreprint

Zeng, Z., Wang, J., Cheng, N., & Xiao, J. Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.01684 · Cited by 2

FBWave: Efficient and Scalable Neural Vocoders for Streaming Text-To-Speech on the EdgePreprint

Wu, B., He, Q., Zhang, P., Koehler, T., Keutzer, K., & Vajda, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.12985 · Cited by 2

The AS-NU System for the M2VoC ChallengePreprint

Hu, C.-H., Wu, Y.-C., Huang, W.-C., Peng, Y., Chen, Y., Ku, P.-J., Toda, T., Tsao, Y., & Wang, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.03009 · Cited by 2

Multi-rate attention architecture for fast streamable Text-to-speech spectrum modelingPreprint

He, Q., Xiu, Z., Koehler, T., & Wu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00705 · Cited by 2

Fast DCTTS: Efficient Deep Convolutional Text-to-SpeechPreprint

Kang, M., Lee, J., Kim, S., & Kim, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00624 · Cited by 2

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

(2022) · doi:10.18653/v1/2022.naacl-demo · Cited by 2

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and UnderstandingPreprint

Ferilli, S., Pierro, D. D., Redavid, D., & Bernasconi, E. (2023) · Preprints.org · doi:10.20944/preprints202308.0062.v1 · Cited by 2

Mamba Neural Operator: Who Wins? Transformers vs. State-Space Models for PdesPreprint

Cheng, C.-W., Huang, J., Zhang, Y., Yang, G., Schönlieb, C., & Avilés-Rivero, A. I. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5257727 · Cited by 2

EditSinger: Zero-Shot Text-Based Singing Voice Editing System with Diverse Prosody Modeling

Zhang, L., Zhao, Z., Ren, Y., & Deng, L. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/625 · Cited by 2

RWEN-TTS: Relation-Aware Word Encoding Network for Natural Text-to-Speech Synthesis

Oh, S., Noh, H., Hong, Y., & Oh, I. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26575 · Cited by 1

Adversarial Learning of Intermediate Acoustic Feature for End-to-End Lightweight Text-to-Speech

Yoon, H., Um, S., Kim, C., & Kang, H.-G. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-1571 · Cited by 1

Development of the Multimodal Handling Interface Based on Google API

Basystiuk, O., & Melnykova, N. (2024) · Computer Design Systems Theory and Practice · doi:10.23939/cds2024.01.216 · Cited by 1

Tibetan Speech Synthesis Based on Pre-Traind Mixture Alignment FastSpeech2

Zhou, Q., Xu, X., & Zhao, Y. (2024) · Applied Sciences · doi:10.3390/app14156834 · Cited by 1

Deepfake-Driven Lecture Summary Generation

Chugh, T., Srikanth, H., Sundeep, A., Sukruth, K., & Preethi, P. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673451 · Cited by 2

IBACodec: End-to-end speech codec with intra-inter broad attention

Yang, X., Zhou, J., Yang, D., Wan, Y., Pan, L., & Luo, S. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103979 · Cited by 1

Disentangling Speaker Representations from Intuitive Prosodic Features for Speaker-Adaptative and Prosody-Controllable Speech Synthesis

Cheng, P., Ling, Z.-H., Meng, M., & Wang, Y. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10849063 · Cited by 1

Enhancing Prosody Transfer in Speech Synthesis by Using Prosodically-Aligned References

Liu, L. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890345 · Cited by 1

Improving Robustness of Diffusion-Based Zero-Shot Speech Synthesis via Stable Formant Generation

Han, C., Lee, S., Nam, G., & Chae, G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889756 · Cited by 1

PriorSinger: Singing Voice Synthesis Model with Prior Condition Cross Attention

Zhang, Z., Yan, B., Cao, Y., & Wang, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887689 · Cited by 1

An Automated Method to Correct Artifacts in Neural Text-to-Speech Models

Jeong, S., Sung, J. S., Hwang, I.-C., & Choi, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3550718 · Cited by 1

Acoustic Analysis of Prosodic Features in Natural versus Synthesized Speech Samples from YourTTS and SYNTACC Models

Galdino, J. C., Araújo, G., Cândido, A., Júnior, M. J. A. D. O., Ponti, M. A., & Aluísio, S. M. (2024) · doi:10.5753/eniac.2024.245092 · Cited by 1

Natural-Prosodic Cross-Lingual Personalized TTS

Chan, D.-Y., Chen, C., Chou, J. E., Zhong, J., Tsai, H.-C., & Wang, J.-F. (2024) · International Conference on Orange Technology (ICOT) · doi:10.1109/icot64290.2024.10936960 · Cited by 1

MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching

Wang, Y., Han, X., Lv, S., Zhou, T., & Chu, Y. (2025) · Applied Sciences · doi:10.3390/app15105503 · Cited by 1

Mongolian Emotional Speech Synthesis Based on CGAN and Improved FastSpeech2

Ren, Q.-D.-E.-J., Yang, Y., & Wang, L. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3749102 · Cited by 1

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

Enkidu: Universal Frequential Perturbation for Real-Time Audio Privacy Protection against Voice Deepfakes

Feng, Z., Chen, J., Zhou, C., Pu, Y., Li, Q., Du, T., & Ji, S. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755629 · Cited by 4

SWAR: A Longformer-Based Gan Vocoder for Gujarati Language

Purohit, R. M., & Patil, H. A. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226237 · Cited by 1

The last call for authenticity: AI reshaping voice fraud landscape

Sobolev, A. (2025) · Journal of Cyber Policy · doi:10.1080/23738871.2025.2597191 · Cited by 1

DisfluencySpeech – Single-Speaker Conversational Speech Dataset with Paralanguage

Wang, K., & Herremans, D. (2024) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon61640.2024.10902687 · Cited by 1

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

STFT-GradTTS: a robust, diffusion-based speech synthesis system with iSTFT decoder for Bangla

Intesum, M., Masud, A. I., Karim, R., & Islam, M. A. (2026) · International Journal of Speech Technology · doi:10.1007/s10772-025-10241-w · Cited by 1

DAWN: Dual-Axial Warped Networks for Sepsis Early Prediction

Jiao, C., Yu, Y., Ouyang, C., Liu, Y., & Liu, Z. L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357128 · Cited by 1

Expressive Text-to-Speech Using LSTM Acoustic Model and EmoV-DB for Emotion-Rich Voice Synthesis

Nithya, A., Lokesh, S., & Prince, A. (2025) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf65644.2025.11379527 · Cited by 1

UniVoice: a unified framework for text-to-speech, singing voice synthesis, and opera singing synthesis

Zhou, Y., Bai, P., & Shi, X. (2026) · Applied Intelligence · doi:10.1007/s10489-026-07137-w · Cited by 1

AutomataGPT: Transformer‐Based Forecasting and Ruleset Inference for Two‐Dimensional Cellular Automata

Berkovich, J., David, N. S., & Buehler, M. J. (2026) · Advanced Science · doi:10.1002/advs.202511352 · Cited by 6

An investigation of the robustness of flow- and diffusion-based speech generation models on noisy transcriptions

Feng, J., Yasuda, Y., & Toda, T. (2026) · APSIPA Transactions on Signal and Information Processing · doi:10.1108/atsip-12-2025-0109 · Cited by 1

Does speech prosody shape social perception equally for AI and human voices? A 16-attribute rating study

Chen, W., Pell, M. D., & Jiang, X. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101142 · Cited by 2

Module Comparison Of Transformer-Tts For Speaker Adaptation Based On Fine-Tuning

Inoue, K., Hara, S., & Abe, M. (2020) · Cited by 1

PAMA-TTS: Progression-Aware Monotonic Attention for Stable SEQ2SEQ TTS with Accurate Phoneme Duration Control

He, Y., Luan, J., & Wang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746202 · Cited by 1

Generation of Variable-Length Time Series from Text using Dynamic Time Warping-Based Method

Ideno, A., Mukuta, Y., & Harada, T. (2021) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3469877.3495644 · Cited by 1

The Impact of Using Synthetically Generated Listening Stimuli on Test‐Taker Performance: A Case Study With Multiple‐Choice , Single‐Selection Items

Choi, I., & Zu, J. (2022) · ETS Research Report Series · doi:10.1002/ets2.12347 · Cited by 1

Speech Audio Corrector: using speech from non-target speakers for one-off correction of mispronunciations in grapheme-input text-to-speech

Fong, J., Lyth, D., Henter, G. E., Tang, H., & King, S. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10138 · Cited by 1

Estonian Text-to-Speech Synthesis with Non-autoregressive Transformers

Rätsep, L., Lellep, R., & Fishel, M. (2022) · Baltic Journal of Modern Computing · doi:10.22364/bjmc.2022.10.3.17 · Cited by 1

Object Detection and Recognition for Visually Impaired Using Cross-Stage-Partial Network Algorithms

Mukherjee, S., Sharma, T., Singh, A., & Dhanalakshmi, S. (2023) · International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) · doi:10.1109/raeeucci57140.2023.10134256 · Cited by 1

Improving Autoregressive NLP Tasks via Modular Linearized Attention

Agostinelli, V., & Chen, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43421-1_6 · Cited by 1

SCRAPS: Speech Contrastive Representations of Acoustic and Phonetic Spaces

Vallés-Ṕerez, I., Beringer, G., Biliński, P., Cook, G., & Barra-Chicote, R. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230540 · Cited by 2

Design and Evaluation of Speech Processing Systems for Meetei/Meitei Mayek

Pangsatabam, H., Chanu, Y. J., & Singh, N. K. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-4713-3_33 · Cited by 1

Exploring the Impact of Speech AI: A Comparative Analysis of ML Models on Arabic Dataset

Mna, M. K. B., & Letaïfa, A. B. (2023) · International Conference on Communications and Networking (ComNet) · doi:10.1109/comnet60156.2023.10366659 · Cited by 1

Speech Processing for Arabic Speech Synthesis Based on Concatenation Rules

Imedjdouben, F. (2024) · SN Computer Science · doi:10.1007/s42979-024-02649-z · Cited by 1

Fastmandarin: Efficient Local Modeling for Natural Mandarin Speech Synthesis

Jiang, C., Gao, Y., Jin, H., Pan, L., & Ng, W. W. Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446112 · Cited by 1

Transformer-powered surrogates close the ICF simulation-experiment gap with extremely limited data

Olson, M., Liu, S., Thiagarajan, J. J., Kustowski, B., Wong, W., & Anirudh, R. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad4e03 · Cited by 2

A CNN-Transformer Knowledge Tracking Model Combining with Forgotten Factors

Huang, X., Wu, Y., & Luo, Y. (2024) · International Conference on Computer Science and Technologies in Education (CSTE) · doi:10.1109/cste62025.2024.00040 · Cited by 1

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

E$^{3}$TTS: End-to-End Text-Based Speech Editing TTS System and Its Applications

Liang, Z., Ma, Z., Du, C., Yu, K., & Chen, X. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3485466 · Cited by 1

PhonemeVec: A Phoneme-Level Contextual Prosody Representation For Speech Synthesis

Wang, S., Chen, L., Ai, Y., Hu, Y., & Ling, Z.-H. (2025) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3711828 · Cited by 1

An Investigation on the Speech Recovery from EEG Signals Using Transformer

Mizuno, T., Kishida, T., Yoshimura, N., & Nakashika, T. (2024) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10848702 · Cited by 1

Utilizing Neural Transducers for Two-Stage Text-to-Speech via Semantic Token Prediction

Kim, M., Jeong, M., Choi, B. J., Kim, S., Lee, J. Y., & Kim, N. S. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3566247 · Cited by 1

Phoneme-Level Duration Controllable Neural Text-to-Speech With Phoneme Embedding Skip Connection and Modified Gaussian Duration Modeling

Ogura, T., Okamoto, T., Ohtani, Y., Cooper, E., Toda, T., & Kawai, H. (2025) · IEEE Access · doi:10.1109/access.2025.3585135 · Cited by 1

LifeGPT: topology-agnostic generative pretrained transformer model for cellular automata

Berkovich, J., & Buehler, M. J. (2025) · npj Artificial Intelligence · doi:10.1038/s44387-025-00014-w · Cited by 7

On the Identification of FOSD-based Non-zero Onset Speech Dataset

Tran, D. C., & Ibrahim, R. (2020) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored50371.2020.9251018 · Cited by 1

Tibetan speech synthesis based on an improved neural network

Ding, Y., Cai, R., & Gong, B. (2021) · MATEC Web of Conferences · doi:10.1051/matecconf/202133606012 · Cited by 1

A Study on Neural-Network-Based Text-to-Speech Adaptation Techniques for Vietnamese

Phuong, P. N., Quang, C. T., Truong, Q. C., & Luong, M. C. (2021) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda202152914.2021.9660445 · Cited by 1

Simultaneous Speech-to-Speech Translation System with Transformer-Based Incremental ASR, MT, and TTS

Fukuda, R., Novitasari, S., Oka, Y., Kano, Y., Yano, Y., Ko, Y., Tokuyama, H., Doi, K., Yanagita, T., Sakti, S., et al. (2021) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda202152914.2021.9660477 · Cited by 1

Effective Data Augmentation Methods for Neural Text-to-Speech Systems

Oh, S., Kwon, O., Hwang, M.-J., Kim, J.-M., & Song, E. (2022) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic54506.2022.9748515 · Cited by 1

Cross-Lingual Transfer for Speech Processing Using Acoustic Language Similarity

Wu, P., Shi, J., Zhong, Y., Watanabe, S., & Black, A. W. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688276 · Cited by 1

Multi-band discriminant speech synthesis analysis based on Natural Language Processing

Liu, S., Yao, Y., Liu, B., Zhu, R., & Ren, J. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778506 · Cited by 1

Efficient decoding self-attention for end-to-end speech synthesis

Zhao, W., & Li, X. (2022) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2100501 · Cited by 1

Close to Human Quality TTS with TransformerPreprint

Li, N., Liu, S., Liu, Y., Zhao, S., Liu, M., & Zhou, M. (2018) · arXiv (Cornell University) · Cited by 78

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

Natural Text-to-Speech Synthesis by Conditioning Spectrogram Predictions from Transformer Network on WaveGlow Vocoder

Sanjay, G., Sooraj, K. C., & Mishra, D. (2020) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi51676.2020.9311564 · Cited by 6

s-Transformer: Segment-Transformer for Robust Neural Speech SynthesisPreprint

Wang, X., Ming, H., He, L., & Soong, F. K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08480 · Cited by 3

Improving End-to-End Speech Synthesis with Local Recurrent Neural Network Enhanced Transformer

Zheng, Y., Li, X., Xie, F., & Lü, L. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054148 · Cited by 29