11 citations · 55 across the 50 of their papers we have counts for
54 papers
TTS-Q: Speaker-Aware Post-Training Quantization of Text-to-Speech Diffusion Transformers
Xiaoqin Feng, Yuqi Li, Weilun Feng +9
Zero-shot text-to-speech (TTS) can clone a voice from seconds of audio. Yet the quality comes from billion-parameter diffusion transformers whose size and multi-step sampling put t…
ParaAnya: Accelerating Parallel Diffusion Sampling with Plug-and-Play Output Caching
Chee-En Yu, Xiao-Xi Tan, Yi-Cheng Lin +3
Diffusion models have achieved remarkable success in generative tasks, but their inherently sequential sampling process introduces a severe computational bottleneck. Recent Paralle…
Listen, Critique, and Refine: RL-Based Self-Refinement for Instruction-Following Speech Synthesis
Chee-En Yu, Yi-Cheng Lin, Sung-Feng Huang +4
Large Audio Language Models (LALMs) can follow diverse instructions to synthesize speech in specified styles. However, complex instructions that require simultaneous control over p…
AudioICL-Bench: A Benchmark for Large Audio Language Model In-Context Learning
Jia-Hung Chen, Yi-Cheng Lin, Kai-Wei Chang +2
In-context learning (ICL) promises training-free adaptation for audio, where labeling every new condition is costly. Yet existing audio ICL studies largely measure Task Recognition…
Findings of the First Teaching Monster Challenge: A Benchmark of Pedagogical Content Knowledge in AI Agents
Yi-Cheng Lin, Yu-Kai Guo, Szu-Chi Chen +15
AI agents can now solve problems, answer like subject experts, and generate long-form multimodal content. However, whether they can adapt a lesson to fit a specified learner, which…
EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration
Jia-Kai Dong, Yi-Cheng Lin, Hung-yi Lee
Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality. Existing automatic judges do not fully addr…
AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition
Yuqi Li, Yi-Cheng Lin, Xianglong Wang +5
On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher…
Hearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language Models
Yun-Shao Tsai, Chun-Wei Chen, Chee-En Yu +2
Sound symbolism, the human tendency to map speech sounds to perceptual qualities such as roundness or sharpness, arises primarily from the acoustics of speech rather than spelling.…
Escaping the Procrustean Bed: Groupwise Orthogonal Connectors for Audio-Language Models
Ho-Lam Chung, Ke-Han Lu, Yi-Cheng Lin +3
Audio-language models compress a speech encoder's output through a Querying Transformer (Q-Former) connector before feeding it to a large language model. We identify two failures i…
Speaker Identity in Non-Verbal Vocalizations: Conditional Distillation and Mixture of Experts Approach
Tzu-Chieh Wei, Yi-Cheng Lin, Huang-Cheng Chou +4
As expressive text-to-speech (TTS) and voice conversion (VC) systems increasingly generate non-verbal vocalizations (NVVs) to enhance naturalness, reliable speaker verification (SV…
Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI
Yi-Cheng Lin, Yun-Shao Tsai, Kuan-Yu Chen +6
Speech technologies are deployed in high-stakes settings, yet fairness concerns remain fragmented across tasks and disciplines. Existing surveys either adopt a general machine-lear…
MoVE: Translating Laughter and Tears via Mixture of Vocalization Experts in Speech-to-Speech Translation
Szu-Chi Chen, I-Ning Tsai, Yi-Cheng Lin +2
Recent Speech-to-Speech Translation (S2ST) systems achieve strong semantic accuracy yet consistently strip away non-verbal vocalizations (NVs), such as laughter and crying that con…
The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation
Yun-Shao Tsai, Yi-Cheng Lin, Huang-Cheng Chou +5
Objective metrics for emotional expressiveness are vital for speech generation, particularly in expressive synthesis and voice conversion requiring emotional prosody transfer. To q…
VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
Yi-Cheng Lin, Yusuke Hirota, Sung-Feng Huang +1
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely…
The Binding Effect: Analyzing How Multi-Dimensional Cues Form Gender Bias in Instruction TTS
Kuan-Yu Chen, Yi-Cheng Lin, Po-Chung Hsieh +5
Current bias evaluations in Instruction Text-to-Speech (ITTS) often rely on univariate testing, overlooking the compositional structure of social cues. In this work, we investigate…
How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang +13
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-…
MOS-Bias: From Hidden Gender Bias to Gender-Aware Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +5
The Mean Opinion Score (MOS) serves as the standard metric for speech quality assessment, yet biases in human annotations remain underexplored. We conduct the first systematic anal…
How Contrastive Decoding Enhances Large Audio Language Models
Tzu-Quan Lin, Wei-Ping Huang, Yi-Cheng Lin +1
While Contrastive Decoding (CD) has been proposed to enhance Large Audio Language Models (LALMs), it has not been evaluated at scale, and the underlying mechanisms driving its succ…
TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
Kai-Wei Chang, Yi-Cheng Lin, Huang-Cheng Chou +9
Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many languages remain underrepresented due to limited resources. In this paper, we intro…
Latent-Mark: An Audio Watermark Robust to Neural Codec Compression
Yen-Shan Chen, Shih-Yu Lai, Ying-Jung Tsou +5
While existing audio watermarking techniques have achieved strong robustness against traditional digital signal processing (DSP) attacks, they remain vulnerable to neural compressi…
TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling
Hao-Hui Xie, Ho-Lam Chung, Yi-Cheng Lin +4
Large Audio-Language Models (LALMs) typically struggle with localized dialectal prosody due to the scarcity of specialized corpora. We present TW-Sound580K, a Taiwanese audio-text…
On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation
Chan-Jan Hsu, Liang-Hsuan Tseng, Yi-Cheng Lin +5
Generative spoken language models pretrained on large-scale raw audio can continue a speech prompt with appropriate content while preserving attributes like speaker and emotion, se…
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition
Yi-Cheng Lin, Yu-Hsuan Li Liang, Hsuan Su +4
Robust ASR under domain shift is crucial because real-world systems encounter unseen accents and domains with limited labeled data. Although pseudo-labeling offers a practical work…
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…