8 citations · 9 across the 13 of their papers we have counts for
14 papers
Learning to Prefer Reliably: Error-Augmented Emotion Preference Optimization with Calibrated Fusion
Zilong Huang, Junyi Peng, Junjie Li +5
Emotion preference learning uses pairwise comparisons between candidate descriptions to align multimodal large language models (MLLMs) with human judgments of open-ended emotion de…
SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification
Junyi Peng, Oldřich Plchot, Xiao Song +9
Modern speaker verification (SV) systems rely on speaker embeddings that are effective but difficult to interpret or query in natural language. Most existing speech-text corpora ta…
WeDefense: A Toolkit to Defend Against Fake Audio
Lin Zhang, Johan Rohdin, Xin Wang +8
The advances in generative AI have enabled the creation of synthetic audio which is perceptually indistinguishable from real, genuine audio. Although this stellar progress enables…
BUT Systems for WildSpoof Challenge: SASV in the Wild
Junyi Peng, Jin Li, Johan Rohdin +3
This paper presents the BUT submission to the WildSpoof Challenge, focusing on the Spoofing-robust Automatic Speaker Verification (SASV) track. We propose a SASV framework designed…
BUT Systems for Environmental Sound Deepfake Detection in the ESDD 2026 Challenge
Junyi Peng, Lin Zhang, Jin Li +2
This paper describes the BUT submission to the ESDD 2026 Challenge, specifically focusing on Track 1: Environmental Sound Deepfake Detection with Unseen Generators. To address the…
Hybrid Pruning: In-Situ Compression of Self-Supervised Speech Models for Speaker Verification and Anti-Spoofing
Junyi Peng, Lin Zhang, Jiangyu Han +5
Although large-scale self-supervised learning (SSL) models like WavLM have achieved state-of-the-art performance in speech processing, their significant size impedes deployment on…
Analysis of ABC Frontend Audio Systems for the NIST-SRE24
Sara Barahona, Anna Silnova, Ladislav Mošner +14
We present a comprehensive analysis of the embedding extractors (frontends) developed by the ABC team for the audio track of NIST SRE 2024. We follow the two scenarios imposed by N…
TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
Junyi Peng, Takanori Ashihara, Marc Delcroix +4
Self-supervised learning (SSL) models have significantly advanced speech processing tasks, and several benchmarks have been proposed to validate their effectiveness. However, previ…
SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline
Helin Wang, Jiarui Hai, Dongchao Yang +7
Target Speech Extraction (TSE) aims to isolate a target speaker's voice from a mixture of multiple speakers by leveraging speaker-specific cues, typically provided as auxiliary aud…
ESPnet-SpeechLM: An Open Speech Language Model Toolkit
Jinchuan Tian, Jiatong Shi, William Chen +13
We present ESPnet-SpeechLM, an open toolkit designed to democratize the development of speech language models (SpeechLMs) and voice-driven agentic applications. The toolkit standar…
Investigation of Speaker Representation for Target-Speaker Speech Processing
Takanori Ashihara, Takafumi Moriya, Shota Horiguchi +5
Target-speaker speech processing (TS) tasks, such as target-speaker automatic speech recognition (TS-ASR), target speech extraction (TSE), and personal voice activity detection (p-…
State-of-the-art Embeddings with Video-free Segmentation of the Source VoxCeleb Data
Sara Barahona, Ladislav Mošner, Themos Stafylakis +4
In this paper, we refine and validate our method for training speaker embedding extractors using weak annotations. More specifically, we use only the audio stream of the source Vox…
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification
Junyi Peng, Ladislav Mošner, Lin Zhang +4
Self-supervised learning (SSL) models for speaker verification (SV) have gained significant attention in recent years. However, existing SSL-based SV systems often struggle to capt…
BUT Systems and Analyses for the ASVspoof 5 Challenge
Johan Rohdin, Lin Zhang, Oldřich Plchot +8
This paper describes the BUT submitted systems for the ASVspoof 5 challenge, along with analyses. For the conventional deepfake detection task, we use ResNet18 and self-supervised…