Experimental results show that VALL-E outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity and could preserve the speaker’s emotion and acoustic environment from the prompt in synthesis.
Text-to-speech systems are improving fast, but measuring how natural they sound still requires expensive human listening tests. Existing automatic methods struggle to generalise well across different datasets. We present QMOS, a MOS prediction framework that extracts hierarchical speech quality features from a frozen Q...
Ravi Sastry Kolluru, S. Devarakonda, S. Radhe Shyam Salopanthula et al.· International Conference on...· 0 citations
A multilingual zero-shot TTS system that supports 14 languages and performs both intra-lingual and cross-lingual reference cloning without requiring transcripts of audio prompts, which achieves the best average overall rank in human evaluation among recent open-source and commercial systems.
Hua-Xuan Wang, Hui-Min Wang, Ruiyu Zhang et al.· 0 citations
Experimental results show that the intelligent error correction model significantly improves the objective quality and subjective listening experience of German speech synthesis without sacrificing real-time performance.
Xingyan Lu· The 2026 International Confe...· 0 citations
This work trains an audio-native interface for DiffusionGemma, a 26B mixture-of-experts model that generates text by uniform, random-token discrete diffusion rather than the absorbing-mask scheme common to recent diffusion language models.
Harsha Vardhan Khurdula, Abhinav Singh, Yoeven D. Khemlani et al.· arXiv.org· 0 citations
IAAN, Identifying and Amplifying Acoustic Neurons, a training-free and label-free method that scores each feed-forward neuron in the audio encoder by contrasting its activation on the real waveform with that on a noise reference lacking the real audio's acoustic information, improves average accuracy and improves a mod...
Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu et al.· 0 citations
ParaASR is introduced, an ASR system that leverages Multi-Token Prediction (MTP) to let a 4B LLM decoder emit multiple tokens per forward step and shows that decoder scaling, low-latency inference, and long-context transcription need not be competing goals when future-token proposals are anchored by the acoustic signal...
