10 citations · 16 across the 12 of their papers we have counts for
12 papers
Anonymization, Not Elimination: Utility-Preserved Speech Anonymization
Yunchong Xiao, Yuxiang Zhao, Ziyang Ma +4
The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degrade data utility, for example b…
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
TASU2: Controllable CTC Simulation for Alignment and Low-Resource Adaptation of Speech LLMs
Jing Peng, Chenghao Wang, Yi Yang +5
Speech LLM post-training increasingly relies on efficient cross-modal alignment and robust low-resource adaptation, yet collecting large-scale audio-text pairs remains costly. Text…
NVV-SuperBench: Beyond Words, Beyond Quality-Benchmarking Nonverbal Vocalizations in Speech Generation
Liumeng Xue, Weizhen Bian, Jiahao Pan +9
Nonverbal vocalizations (NVVs), such as laughing, sighing, and sobbing, are essential for human-like speech, yet standardized evaluation rarely jointly assesses whether systems gen…
AHAMask: Reliable Task Specification for Large Audio Language Models without Instructions
Yiwei Guo, Bohan Li, Hankun Wang +4
Although current large audio language models (LALMs) extend text large language models (LLMs) with generic acoustic understanding abilities, they usually suffer from prompt sensiti…
Traceable TTS: Toward Watermark-Free TTS with Strong Traceability
Yuxiang Zhao, Yunchong Xiao, Yushen Chen +4
Recent advances in Text-To-Speech (TTS) technology have enabled synthetic speech to mimic human voices with remarkable realism, raising significant security concerns. This undersco…
A Survey on Speech Large Language Models for Understanding
Jing Peng, Yucheng Wang, Bohan Li +9
Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that…
vec2wav 2.0: Advancing Voice Conversion via Discrete Token Vocoders
Yiwei Guo, Zhihan Li, Junjie Li +5
We propose a new speech discrete token vocoder, vec2wav 2.0, which advances voice conversion (VC). We use discrete tokens from speech self-supervised models as the content features…
On the Effectiveness of Acoustic BPE in Decoder-Only TTS
Bohan Li, Feiyu Shen, Yiwei Guo +3
Discretizing speech into tokens and generating them by a decoder-only model have been a promising direction for text-to-speech (TTS) and spoken language modeling (SLM). To shorten…
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge
Yiwei Guo, Chenrun Wang, Yifan Yang +9
Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice sy…
Attention-Constrained Inference for Robust Decoder-Only Text-to-Speech
Hankun Wang, Chenpeng Du, Yiwei Guo +3
Recent popular decoder-only text-to-speech models are known for their ability of generating natural-sounding speech. However, such models sometimes suffer from word skipping and re…
VALL-T: Decoder-Only Generative Transducer for Robust and Decoding-Controllable Text-to-Speech
Chenpeng Du, Yiwei Guo, Hankun Wang +6
Recent TTS models with decoder-only Transformer architecture, such as SPEAR-TTS and VALL-E, achieve impressive naturalness and demonstrate the ability for zero-shot adaptation give…