8 citations · 26 across the 16 of their papers we have counts for
6 papers · 1 filter
GSQA: An End-to-End Model for Generative Spoken Question Answering
Min-Han Shih, Ho-Lam Chung, Yu-Chi Pai +4
In recent advancements in spoken question answering (QA), end-to-end models have made significant strides. However, previous research has primarily focused on extractive span selec…
Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model
Kai-Wei Chang, Ming-Hsin Chen, Yun-Ping Lin +5
Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and f…
Exploring In-Context Learning of Textless Speech Language Model for Speech Classification Tasks
Ming-Hao Hsu, Kai-Wei Chang, Shang-Wen Li +1
Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an essential role in utilizing large language models (LLMs)…
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…
AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models
Yuan Tseng, Layne Berry, Yi-Ting Chen +16
Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models of…
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…