352 citations · 459 across the 9 of their papers we have counts for
9 papers
A Large-Scale Evaluation of Speech Foundation Models
Shu-wen Yang, Heng-Jui Chang, Zili Huang +18
The foundation model paradigm leverages a shared foundation model to achieve state-of-the-art (SOTA) performance for various tasks, requiring minimal downstream-specific modeling a…
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning
Hsuan Su, Pohan Chi, Shih-Cheng Huang +4
Much literature has shown that prompt-based learning is an efficient method to make use of the large pre-trained language model. Recent works also exhibit the possibility of steeri…
Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction
Wei-Cheng Tseng, Po-Han Chi, Jia-Hua Wu +1
The capability of accurate prediction of protein functions and properties is essential in the biotechnology industry, e.g. drug development and artificial protein synthesis, etc. T…
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
Yi-Chen Chen, Po-Han Chi, Shu-wen Yang +7
There is a wide variety of speech processing tasks ranging from extracting content information from speech signals to generating speech signals. For different tasks, model networks…
SUPERB: Speech processing Universal PERformance Benchmark
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang +17
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large…
Input-independent Attention Weights Are Expressive Enough: A Study of Attention in Self-supervised Audio Transformers
Tsung-Han Wu, Chun-Chen Hsieh, Yen-Hao Chen +2
In this paper, we seek solutions for reducing the computation complexity of transformer-based models for speech representation learning. We evaluate 10 attention algorithms; then,…
Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation
Po-Han Chi, Pei-Hung Chung, Tsung-Han Wu +4
For self-supervised speech processing, it is crucial to use pretrained models as speech representation extractors. In recent works, increasing the size of the model has been utiliz…
BERT's output layer recognizes all hidden layers? Some Intriguing Phenomena and a simple way to boost BERT
Wei-Tsung Kao, Tsung-Han Wu, Po-Han Chi +2
Although Bidirectional Encoder Representations from Transformers (BERT) have achieved tremendous success in many natural language processing (NLP) tasks, it remains a black box. A…
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
Andy T. Liu, Shu-wen Yang, Po-Han Chi +2
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous spe…