10 citations · 26 across the 14 of their papers we have counts for
14 papers
SLM-TTA: A Framework for Test-Time Adaptation of Generative Spoken Language Models
Yuan-Kuei Wu, Yang Liu, Yiteng Huang +9
Spoken Language Models (SLMs) are increasingly central to modern speech-driven applications, but performance degrades under acoustic shift - real-world noise, reverberation, and mi…
Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback
Jianglin Lu, Yuanwei Wu, Ziyi Zhao +4
Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
Yuan-Kuei Wu, Juan Azcarreta, Kashyap Patel +4
This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergen…
Codec-SUPERB @ SLT 2024: A lightweight benchmark for neural audio codec models
Haibin Wu, Xuanjun Chen, Yi-Cheng Lin +13
Neural audio codec models are becoming increasingly important as they serve as tokenizers for audio, enabling efficient transmission or facilitating speech language modeling. The i…
SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks
Kai-Wei Chang, Haibin Wu, Yu-Kai Wang +6
Prompting has become a practical method for utilizing pre-trained language models (LMs). This approach offers several advantages. It allows an LM to adapt to new tasks with minimal…
Codec-SUPERB: An In-Depth Analysis of Sound Codec Models
Haibin Wu, Ho-Lam Chung, Yi-Cheng Lin +7
The sound codec's dual roles in minimizing data transmission latency and serving as tokenizers underscore its critical importance. Recent years have witnessed significant developme…
SpeechGen: Unlocking the Generative Power of Speech Language Models with Prompts
Haibin Wu, Kai-Wei Chang, Yuan-Kuei Wu +1
Large language models (LLMs) have gained considerable attention for Artificial Intelligence Generated Content (AIGC), particularly with the emergence of ChatGPT. However, the direc…
On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting
Wei-Tsung Kao, Yuan-Kuei Wu, Chia-Ping Chen +3
User-defined keyword spotting is a task to detect new spoken terms defined by users. This can be viewed as a few-shot learning problem since it is unreasonable for users to define…
Improving the transferability of speech separation by meta-learning
Kuan-Po Huang, Yuan-Kuei Wu, Hung-yi Lee
Speech separation aims to separate multiple speech sources from a speech mixture. Although speech separation is well-solved on some existing English speech separation benchmarks, i…
Multi-accent Speech Separation with One Shot Learning
Kuan-Po Huang, Yuan-Kuei Wu, Hung-yi Lee
Speech separation is a problem in the field of speech processing that has been studied in full swing recently. However, there has not been much work studying a multi-accent speech…
One Shot Learning for Speech Separation
Yuan-Kuei Wu, Kuan-Po Huang, Yu Tsao +1
Despite the recent success of speech separation models, they fail to separate sources properly while facing different sets of people or noisy environments. To tackle this problem,…
SADDEL: Joint Speech Separation and Denoising Model based on Multitask Learning
Yuan-Kuei Wu, Chao-I Tuan, Hung-yi Lee +1
Speech data collected in real-world scenarios often encounters two issues. First, multiple sources may exist simultaneously, and the number of sources may vary with time. Second, t…
MITAS: A Compressed Time-Domain Audio Separation Network with Parameter Sharing
Chao-I Tuan, Yuan-Kuei Wu, Hung-yi Lee +1
Deep learning methods have brought substantial advancements in speech separation (SS). Nevertheless, it remains challenging to deploy deep-learning-based models on edge devices. Th…