13 papers · 1 filter
Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling
Ye-Xin Lu, Xin Wang, Yang Ai +3
Recent zero-shot text-to-speech (TTS) systems achieve remarkable naturalness and speaker similarity but typically require high-quality speaker prompts and either strip away or enta…
DAIEN-TTS: Disentangled Audio Infilling for Environment-Aware Text-to-Speech Synthesis
Ye-Xin Lu, Yu Gu, Kun Wei +3
This paper presents DAIEN-TTS, a zero-shot text-to-speech (TTS) framework that enables ENvironment-aware synthesis through Disentangled Audio Infilling. By leveraging separate spea…
Is GAN Necessary for Mel-Spectrogram-based Neural Vocoder?
Hui-Peng Du, Yang Ai, Rui-Chen Zheng +2
Recently, mainstream mel-spectrogram-based neural vocoders rely on generative adversarial network (GAN) for high-fidelity speech generation, e.g., HiFi-GAN and BigVGAN. However, th…
Improving Noise Robustness of LLM-based Zero-shot TTS via Discrete Acoustic Token Denoising
Ye-Xin Lu, Hui-Peng Du, Fei Liu +2
Large language model (LLM) based zero-shot text-to-speech (TTS) methods tend to preserve the acoustic environment of the audio prompt, leading to degradation in synthesized speech…
Incremental Disentanglement for Environment-Aware Zero-Shot Text-to-Speech Synthesis
Ye-Xin Lu, Hui-Peng Du, Zheng-Yan Sheng +2
This paper proposes an Incremental Disentanglement-based Environment-Aware zero-shot text-to-speech (TTS) method, dubbed IDEA-TTS, that can synthesize speech for unseen speakers wh…
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions
Hui-Peng Du, Ye-Xin Lu, Yang Ai +1
This paper proposes a novel neural denoising vocoder that can generate clean speech waveforms from noisy mel-spectrograms. The proposed neural denoising vocoder consists of two com…