X-VC is a zero‑shot streaming voice conversion system that performs one‑step conversion directly in the latent space of a pretrained neural codec. It employs a dual‑conditioning acoustic converter that jointly models source codec latents and target acoustic conditions, while using adaptive normalization to inject utterance‑level speaker information. The model is trained with generated paired data and a role‑assignment strategy, and uses a chunkwise inference scheme with overlap smoothing to achieve low‑latency streaming inference, achieving superior WER, speaker similarity, and real‑time factor on the Seed‑TTS‑Eval benchmark.
arXiv:2606. 09048v1 Announce Type: cross Abstract: Removing intermediate representations and separately trained decoding stages has become an important direction in generative modeling.
arXiv:2609.38658v1 Announce Type: cross Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but the...
arXiv:2603. 04219v2 Announce Type: replace-cross Abstract: We investigate the use of zero-shot text-to-speech (ZS-TTS) as a data augmentation source for low-resource personalized speech synthesis.
arXiv:2608. 15690v1 Announce Type: cross Abstract: Text-to-audio-video (T2AV) generation models produce a video and its soundtrack from a textual description, but offer no control over whose voice speaks in the output.
arXiv:2604. 19635v2 Announce Type: replace-cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications.
arXiv:2608. 08638v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) now supports interactive assistants, personalized media, and accessibility tools.
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
MeanVoiceFlow2 is a new voice conversion framework that jointly optimizes a flow-based conversion module and a computationally efficient content encoder. It is trained via conversion distillation from MeanVoiceFlow and real data reconstruction, and further enhanced with diffusion-GAN training, sample mixing, and teacher-guided conditioning augmentation. Experiments on zero-shot voice conversion show that MeanVoiceFlow2 delivers higher perceptual quality and about nine times faster inference than its predecessor while preserving speaker similarity.
StyleStream 2.0 is a fast, controllable streaming voice style conversion system that reduces end‑to‑end latency to about 520 ms on a consumer GPU, halving the delay of its predecessor while preserving conversion quality. It replaces mel‑spectrogram context with a compact style embedding, enabling a unified text‑conditioned flow model that supports both text‑based voice design and instruction‑based voice editing. Experiments demonstrate that StyleStream 2.0 achieves superior target style fidelity, voice design, and voice editing performance while maintaining intelligibility and attribute preservation.
arXiv:2605. 30748v2 Announce Type: replace-cross Abstract: We present Chatterbox-Flash, a zero-shot text-to-speech model obtained by fine-tuning a pretrained autoregressive TTS decoder into a block-diffusion decoder, enabling parallel token generation within each block while retaining block-by-block streaming.
arXiv:2606. 08843v1 Announce Type: cross Abstract: We present a voice conversion (VC) framework that utilizes K-Nearest Neighbors (KNN) retrieval over WavLM representations to align non-parallel source and target speech, constructing synthetic training pairs for supervised learning.
arXiv:2607. 08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable.