7 papers
Harness TTS: Towards Context-Aware Expressive Speech Synthesis with Harness Layer
Shengfan Shen, Di Wu, Xingchen Song +5
Expressive speech synthesis for voice assistants requires flexible style control that adapts to explicit requests and broader interaction context. We propose Harness TTS, a lightwe…
F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation
Dinghao Zhou, Xingchen Song, Di Wu +3
Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but…
Borderless Long Speech Synthesis
Xingchen Song, Di Wu, Dinghao Zhou +12
Most existing text-to-speech (TTS) systems either synthesize speech sentence by sentence and stitch the results together, or drive synthesis from plain-text dialogues alone. Both a…
Iterate to Differentiate: Enhancing Discriminability and Reliability in Zero-Shot TTS Evaluation
Shengfan Shen, Di Wu, Xingchen Song +5
Reliable evaluation of modern zero-shot text-to-speech (TTS) models remains challenging. Subjective tests are costly and hard to reproduce, while objective metrics often saturate,…
Back to Ear: Perceptually Driven High Fidelity Music Reconstruction
Kangdi Wang, Zhiyue Wu, Dinghao Zhou +3
Variational Autoencoders (VAEs) are essential for large-scale audio tasks like diffusion-based generation. However, existing open-source models often neglect auditory perceptual as…
TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch
Xingchen Song, Chengdong Liang, Binbin Zhang +9
Large Automatic Speech Recognition (ASR) models demand a vast number of parameters, copious amounts of data, and significant computational resources during the training process. Ho…