34 citations · 67 across the 41 of their papers we have counts for
8 papers · 1 filter
Boundary and Intra-Segment Learning for Partial Audio Deepfake Localization
Zhe Ye, Xiangui Kang, Minhua Huang +3
Partial audio deepfakes manipulate only selected speech regions, making them difficult to be localized. Existing methods exploit boundary cues for partial deepfake localization, bu…
Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models
Nikita Kuzmin, Tao Zhong, Jiajun Deng +6
End-to-end full-duplex speech models feed user audio through an always-on LLM backbone, yet the speaker privacy implications of their hidden representations remain unexamined. Foll…
StreamVoiceAnon+: Emotion-Preserving Streaming Speaker Anonymization via Frame-Level Acoustic Distillation
Nikita Kuzmin, Kong Aik Lee, Eng Siong Chng
We address the challenge of preserving emotional content in streaming speaker anonymization (SA). Neural audio codec language models trained for audio continuation tend to degrade…
The Interspeech 2026 Audio Reasoning Challenge: Evaluating Reasoning Process Quality for Audio Reasoning Models and Agents
Ziyang Ma, Ruiyang Xu, Yinghao Ma +9
Recent Large Audio Language Models (LALMs) excel in understanding but often lack transparent reasoning. To address this "black-box" limitation, we organized the Audio Reasoning Cha…
Stream-Voice-Anon: Enhancing Utility of Real-Time Speaker Anonymization via Neural Audio Codec and Language Models
Nikita Kuzmin, Songting Liu, Kong Aik Lee +1
Protecting speaker identity is crucial for online voice applications, yet streaming speaker anonymization (SA) remains underexplored. Recent research has demonstrated that neural a…
The ICASSP 2026 HumDial Challenge: Benchmarking Human-like Spoken Dialogue Systems in the LLM Era
Zhixian Zhao, Shuiyuan Wang, Guojian Li +12
Driven by the rapid advancement of Large Language Models (LLMs), particularly Audio-LLMs and Omni-models, spoken dialogue systems have evolved significantly, progressively narrowin…
Improving Code-Switching Speech Recognition with TTS Data Augmentation
Yue Heng Yeo, Yuchen Hu, Shreyas Gopal +3
Automatic speech recognition (ASR) for conversational code-switching speech remains challenging due to the scarcity of realistic, high-quality labeled speech data. This paper explo…
DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection
Yuxin Li, Xiangyu Zhang, Yifei Li +4
Speech is a scalable and non-invasive biomarker for early mental health screening. However, widely used depression datasets like DAIC-WOZ exhibit strong coupling between linguistic…