activity
20242026
collaborators

6 papers

cs.SD2026

All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation

Leonardo Haw-Yang Foo, Chih-Kai Yang, Chen-An Li +2

Large Audio-Language Models show consistent performance gains across speech and audio benchmarks, yet high scores may not reflect true auditory perception. If a model can answer qu…

cs.CL2025

Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang +77

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spo…

eess.AS2025

SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning

Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4

We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…

cs.CL2025

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…

cs.CL2024

Building a Taiwanese Mandarin Spoken Language Model: A First Attempt

Chih-Kai Yang, Yu-Kuan Fu, Chen-An Li +18

This technical report presents our initial attempt to build a spoken large language model (LLM) for Taiwanese Mandarin, specifically tailored to enable real-time, speech-to-speech…