collaborators

10 papers

cs.SD2026

Clustering Unsupervised Representations as Defense against Poisoning Attacks on Speech Commands Classification System

Thomas Thebaud, Sonal Joshi, Henry Li +4

Poisoning attacks entail attackers intentionally tampering with training data. In this paper, we consider a dirty-label poisoning attack scenario on a speech commands classificatio…

cs.CL2026

Beyond Transcripts: Iterative Peer-Editing with Audio Unlocks High-Quality Human Summaries of Conversational Speech

Kaavya Chaparala, Thomas Thebaud, Jesús Villalba López +3

There are not enough established benchmarks for the task fo speech summarization. Creating new benchmarks demands human annotation, as LLMs could embed systemic errors and bias int…

eess.AS2026

MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances

Junhyeok Lee, Helin Wang, Yaohan Guan +4

We introduce MaskVCT, a zero-shot voice conversion (VC) model that offers multi-factor controllability through multiple classifier-free guidances (CFGs). While previous VC models r…

cs.CL2025

Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization

Yen-Ju Lu, Kunxiao Gao, Mingrui Liang +5

Recent audio language models can follow long conversations. However, research on emotion-aware or spoken dialogue summarization is constrained by the lack of data that links speech…

cs.SD2025

Multi-Target Backdoor Attacks Against Speaker Recognition

Alexandrine Fortier, Sonal Joshi, Thomas Thebaud +3

In this work, we propose a multi-target backdoor attack against speaker identification using position-independent clicking sounds as triggers. Unlike previous single-target approac…

eess.AS2025

Scaling Multi-Talker ASR with Speaker-Agnostic Activity Streams

Xiluo He, Alexander Polok, Jesús Villalba +2

An increasingly common training paradigm for multi-talker automatic speech recognition (ASR) is to use speaker activity signals to adapt single-speaker ASR models for overlapping s…