1.4k citations · 2.9k across the 108 of their papers we have counts for
7 papers · 2 filters
Towards General-Purpose Text-Instruction-Guided Voice Conversion
Chun-Yi Kuan, Chen An Li, Tsu-Yuan Hsu +5
This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike tr…
Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech
Chien-yu Huang, Ke-Han Lu, Shih-Heng Wang +12
Text language models have shown remarkable zero-shot capability in generalizing to unseen tasks when provided with well-formulated instructions. However, existing studies in speech…
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…
ABC-KD: Attention-Based-Compression Knowledge Distillation for Deep Learning-Based Noise Suppression
Yixin Wan, Yuan Zhou, Xiulian Peng +2
Noise suppression (NS) models have been widely applied to enhance speech quality. Recently, Deep Learning-Based NS, which we denote as Deep Noise Suppression (DNS), became the main…
MiniSUPERB: Lightweight Benchmark for Self-supervised Speech Models
Yu-Hsiang Wang, Huang-Yu Chen, Kai-Wei Chang +2
SUPERB was proposed to evaluate the generalizability of self-supervised learning (SSL) speech models across various tasks. However, it incurs high computational costs due to the la…
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks
Kai-Wei Chang, Yu-Kai Wang, Hua Shen +4
Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained language model (LM) to directly generate the output for downstream tasks. Recently, prompt…