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cs.SD2026

FlowGRPO for Vocal Separation: From Reward Analysis to Reward Design

Lishi Zuo, Chong-Xin Gan, Zezhong Jin +3

The Flow Matching objective supervises the velocity field at intermediate states, whereas waveform metrics are evaluated after decoding and may be non-differentiable. To align Flow…

cs.SD2026

TS-SP: Learning Speaker-Preserving Representations in Audio Large Language Models

Junjie Li, Zheng Liang, Zhe Li +2

Audio large language models (ALLMs) can understand speech content, yet their ability to use speaker identity for verification remains limited. We propose TS-SP (Two-Stage Speaker P…

cs.SD2026

A Unified Uncertainty-Aware Back-End for Speaker Verification: Scoring, Normalization, and Calibration

Junjie Li, Kong Aik Lee

Speaker verification back-ends commonly combine similarity scoring, score normalization, and calibration. However, speaker embeddings extracted from real-world utterances have tria…

cs.SD2026

Decoupled Latent Flow Matching for Few-Step Joint Vocal-Accompaniment Separation

Lishi Zuo, Youzhi Tu, Lu Yi +4

Generative modeling provides a flexible way to model mixture-conditioned source distributions, but iterative diffusion and flow matching models are costly for long music signals. T…

cs.MM2026

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…

cs.SD2026

Beyond Residual Connections: Manifold-Constrained Hyper-Connections for Robust Speaker Representation Learning

Zezhong Jin, Xiaoyu Wang, Zhe Li +4

Residual connections are fundamental to deep speaker recogni- tion models, such as ECAPA-TDNN and ResNet. However, standard identity mapping limits information flow to a sin- gle p…

cs.MM2026

EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation

Zilong Huang, Kong Aik Lee, Junjie Li +2

Multimodal emotion recognition in conversation (MERC) can leverage multimodal and contextual cues to boost recognition performance. However, existing fusion approaches in MERC ofte…

cs.MM2026

EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation

Zilong Huang, Kong Aik Lee, Chong-Xin Gan +3

Multimodal emotion recognition in conversation (MERC) achieves accurate predictions by integrating multimodal and contextual information in dialogues. While current MERC approaches…

eess.AS2026

Text-Independent Speaker Verification Using Discrete Audio Tokens

Zheng Liang, Junjie Li, Kong Aik Lee

Neural audio codecs (NACs) enable efficient audio compression and have achieved success in downstream tasks such as speech synthesis. However, their discrete representations consis…

cs.SD2026

Towards Robust Uncertainty-Aware Speaker Modeling

Junjie Li, Yang Xiao, Kong Aik Lee

Speaker embeddings aggregate frame-level acoustic features into compact representations for speaker recognition. Recent uncertainty-aware speaker modeling approaches further charac…

eess.AS2026

UNet-Based Fusion and Exponential Moving Average Adaptation for Noise-Robust Speaker Recognition

Chong-Xin Gan, Peter Bell, Man-Wai Mak +4

The joint training of speech enhancement and speaker embedding networks for speaker recognition is widely adopted under noisy acoustic environments. While effective, this paradigm…