arXiv subjects

Jiayan Cui

Publications and source records attributed to Jiayan Cui.

3 recordsLinked to original sources

arXiv2026

Detector-Guided Vocoder Training for Adversarial Text-to-Speech Synthesis

In this paper, we propose a novel Text-to-Speech (TTS) framework designed to perform adversarial attacks against speech deepfake detection systems. Given a TTS model and a detector capable of classifying its outputs as synthetic, our approach fine-tunes the vocoder by integrating the detector into the TTS training loop. Unlike traditional adversarial methods that rely on post-hoc perturbations or external noise injection, our approach enables the generation of synthetic speech that is intrinsically adversarial, capable of lowering the target detector's reliability without requiring additional post-processing steps. We evaluate the framework in both white-box and black-box settings, showing its effectiveness against known and unseen detectors. We also propose a multi-detector strategy that allows the system to simultaneously learn to deceive multiple detectors, showcasing the scalability of the method. Experimental results confirm the efficacy of our approach, which has the potential to impact forensic security and detection countermeasures. At the same time, subjective listening tests reveal that the proposed method also enhances synthesized speech quality.

cs.SD↗

arXiv2026

Qwen-Audio-3.0-TTS: Freely Controllable and Highly Robust Speech Synthesis with Multi-Stage Training Paradigm

In this report, we present Qwen-Audio-3.0-TTS, a production-oriented speech synthesis system that jointly advances content consistency, speaker similarity, prosodic naturalness, audio quality, controllability, multilingual coverage, efficiency, and robustness. It combines a 12.5~Hz low-frame-rate speech tokenizer for reduced inference latency with a five-stage progressive training paradigm for coordinated language model (LM) and flow-matching model (FM) optimization. The model provides production-level control through free-style natural-language instructions and fine-grained inline tags, while supporting 16 languages, 20 Chinese dialect regions, one-pass long-form synthesis up to 3 minutes, and robust generation from noisy, reverberant, or unclear reference speech. Across SEED-TTS-Eval, CV3-Eval, instruction-following, long-form, and acoustic-robustness evaluations, Qwen-Audio-3.0-TTS achieves state-of-the-art performance on many reported dimensions or the strongest aggregate results. It also ranks first on the independent Artificial Analysis Text-to-Speech Leaderboard. These results establish Qwen-Audio-3.0-TTS as a strong foundation for production-level speech synthesis.

eess.AS↗

arXiv2025

GLM-TTS Technical Report

This work proposes GLM-TTS, a production-level TTS system designed for efficiency, controllability, and high-fidelity speech generation. GLM-TTS follows a two-stage architecture, consisting of a text-to-token autoregressive model and a token-to-waveform diffusion model. With only 100k hours of training data, GLM-TTS achieves state-of-the-art performance on multiple open-source benchmarks. To meet production requirements, GLM-TTS improves speech quality through an optimized speech tokenizer with fundamental frequency constraints and a GRPO-based multi-reward reinforcement learning framework that jointly optimizes pronunciation, speaker similarity, and expressive prosody. In parallel, the system enables efficient and controllable deployment via parameter-efficient LoRA-based voice customization and a hybrid phoneme-text input scheme that provides precise pronunciation control. Our code is available at https://github.com/zai-org/GLM-TTS. Real-time speech synthesis demos are provided via Z.ai (audio.z.ai), the Zhipu Qingyan app/web (chatglm.cn).

cs.SD↗