Text to Speech
Text-to-speech (TTS) research aims to synthesize natural-sounding human speech from textual input, focusing on improving speech quality, speaker similarity, and efficiency. Current efforts concentrate on developing advanced architectures like diffusion models and transformers, often incorporating techniques such as flow matching and semantic communication to enhance both the naturalness and expressiveness of generated speech. This field is crucial for applications ranging from assistive technologies and accessibility tools to combating deepfakes and creating more realistic synthetic datasets for training other AI models.
Papers
Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Philip Anastassiou, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Chen, Jian Cong, Lelai Deng, Chuang Ding, Lu Gao, Mingqing Gong, Peisong Huang, Qingqing Huang, Zhiying Huang, Yuanyuan Huo, Dongya Jia, Chumin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Li, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Liu, Lu Lu, Junjie Pan, Xin Wang, Yuping Wang, Yuxuan Wang, Zhen Wei, Jian Wu, Chao Yao, Yifeng Yang, Yuanhao Yi, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhang, Zilin Zhao, Dejian Zhong, Xiaobin Zhuang
SimpleSpeech: Towards Simple and Efficient Text-to-Speech with Scalar Latent Transformer Diffusion Models
Dongchao Yang, Dingdong Wang, Haohan Guo, Xueyuan Chen, Xixin Wu, Helen Meng
BiVocoder: A Bidirectional Neural Vocoder Integrating Feature Extraction and Waveform Generation
Hui-Peng Du, Ye-Xin Lu, Yang Ai, Zhen-Hua Ling
Faces that Speak: Jointly Synthesising Talking Face and Speech from Text
Youngjoon Jang, Ji-Hoon Kim, Junseok Ahn, Doyeop Kwak, Hong-Sun Yang, Yoon-Cheol Ju, Il-Hwan Kim, Byeong-Yeol Kim, Joon Son Chung
Evaluating Text-to-Speech Synthesis from a Large Discrete Token-based Speech Language Model
Siyang Wang, Éva Székely