activity
20242026
most citedDeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

15 citations · 53 across the 43 of their papers we have counts for

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Showing 2025Show all

16 papers · 1 filter

eess.AS2025

TAU: A Benchmark for Cultural Sound Understanding Beyond Semantics

Yi-Cheng Lin, Yu-Hua Chen, Jia-Kai Dong +12

Large audio-language models are advancing rapidly, yet most evaluations emphasize speech or globally sourced sounds, overlooking culturally distinctive cues. This gap raises a crit…

cs.CL2025

MI-Fuse: Label Fusion for Unsupervised Domain Adaptation with Closed-Source Large-Audio Language Model

Hsiao-Ying Huang, Yi-Cheng Lin, Hung-yi Lee

Large audio-language models (LALMs) show strong zero-shot ability on speech tasks, suggesting promise for speech emotion recognition (SER). However, SER in real-world deployments o…

eess.AS2025

Do You Hear What I Mean? Quantifying the Instruction-Perception Gap in Instruction-Guided Expressive Text-To-Speech Systems

Yi-Cheng Lin, Huang-Cheng Chou, Tzu-Chieh Wei +2

Instruction-guided text-to-speech (ITTS) enables users to control speech generation through natural language prompts, offering a more intuitive interface than traditional TTS. Howe…

cs.SD2025

ASTAR-NTU solution to AudioMOS Challenge 2025 Track1

Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jui-Chiang Wei +3

Evaluation of text-to-music systems is constrained by the cost and availability of collecting experts for assessment. AudioMOS 2025 Challenge track 1 is created to automatically pr…

eess.AS2025

MMMOS: Multi-domain Multi-axis Audio Quality Assessment

Yi-Cheng Lin, Jia-Hung Chen, Hung-yi Lee

Accurate audio quality estimation is essential for developing and evaluating audio generation, retrieval, and enhancement systems. Existing non-intrusive assessment models predict…

eess.AS2025

HighRateMOS: Sampling-Rate Aware Modeling for Speech Quality Assessment

Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +9

Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce bi…

cs.SD2025

A correlation-permutation approach for speech-music encoders model merging

Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jeremy H. M Wong +3

Creating a unified speech and music model requires expensive pre-training. Model merging can instead create an unified audio model with minimal computational expense. However, dire…

eess.AS2025

Multi-Distillation from Speech and Music Representation Models

Jui-Chiang Wei, Yi-Cheng Lin, Fabian Ritter-Gutierrez +1

Real-world audio often mixes speech and music, yet models typically handle only one domain. This paper introduces a multi-teacher distillation framework that unifies speech and mus…

eess.AS2025★ 3 cited

EMO-Debias: Benchmarking Gender Debiasing Techniques in Multi-Label Speech Emotion Recognition

Yi-Cheng Lin, Huang-Cheng Chou, Yu-Hsuan Li Liang +1

Speech emotion recognition (SER) systems often exhibit gender bias. However, the effectiveness and robustness of existing debiasing methods in such multi-label scenarios remain und…

eess.AS2025

CO-VADA: A Confidence-Oriented Voice Augmentation Debiasing Approach for Fair Speech Emotion Recognition

Yun-Shao Tsai, Yi-Cheng Lin, Huang-Cheng Chou +1

Bias in speech emotion recognition (SER) systems often stems from spurious correlations between speaker characteristics and emotional labels, leading to unfair predictions across d…

eess.AS2025★ 3 cited

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach

Yi-Cheng Lin, Huang-Cheng Chou, Hung-yi Lee

While subgroup disparities and performance bias are increasingly studied in computational research, fairness in categorical Speech Emotion Recognition (SER) remains underexplored.…

eess.AS2025★ 1 cited

Meta-PerSER: Few-Shot Listener Personalized Speech Emotion Recognition via Meta-learning

Liang-Yeh Shen, Shi-Xin Fang, Yi-Cheng Lin +2

This paper introduces Meta-PerSER, a novel meta-learning framework that personalizes Speech Emotion Recognition (SER) by adapting to each listener's unique way of interpreting emot…

eess.AS2025

ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality

Yu-Xiang Luo, Yi-Cheng Lin, Ming-To Chuang +9

Despite extensive research on toxic speech detection in text, a critical gap remains in handling spoken Mandarin audio. The lack of annotated datasets that capture the unique proso…

cs.SD2025

Distilling a speech and music encoder with task arithmetic

Fabian Ritter-Gutierrez, Yi-Cheng Lin, Jui-Chiang Wei +4

Despite the progress in self-supervised learning (SSL) for speech and music, existing models treat these domains separately, limiting their capacity for unified audio understanding…

cs.CL2025★ 1 cited

A Preliminary Exploration with GPT-4o Voice Mode

Yu-Xiang Lin, Chih-Kai Yang, Wei-Chih Chen +4

With the rise of multimodal large language models, GPT-4o stands out as a pioneering model, driving us to evaluate its capabilities. This report assesses GPT-4o across various task…

cs.CL2025

BreezyVoice: Adapting TTS for Taiwanese Mandarin with Enhanced Polyphone Disambiguation -- Challenges and Insights

Chan-Jan Hsu, Yi-Cheng Lin, Chia-Chun Lin +10

We present BreezyVoice, a Text-to-Speech (TTS) system specifically adapted for Taiwanese Mandarin, highlighting phonetic control abilities to address the unique challenges of polyp…