Search Results for author: Andy T. Liu

Found 12 papers, 7 papers with code

QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition

no code implementations3 Mar 2022 Andy T. Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, Andrew Arnold

Recently, prompt-based learning for pre-trained language models has succeeded in few-shot Named Entity Recognition (NER) by exploiting prompts as task guidance to increase label efficiency.

few-shot-ner Few-shot NER +2

Don't speak too fast: The impact of data bias on self-supervised speech models

no code implementations15 Oct 2021 Yen Meng, Yi-Hui Chou, Andy T. Liu, Hung-Yi Lee

Self-supervised Speech Models (S3Ms) have been proven successful in many speech downstream tasks, like ASR.

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning

5 code implementations5 Jun 2020 Haibin Wu, Andy T. Liu, Hung-Yi Lee

To explore this issue, we proposed to employ Mockingjay, a self-supervised learning based model, to protect anti-spoofing models against adversarial attacks in the black-box scenario.

Self-Supervised Learning Speaker Verification +1

Understanding Self-Attention of Self-Supervised Audio Transformers

2 code implementations5 Jun 2020 Shu-wen Yang, Andy T. Liu, Hung-Yi Lee

Self-supervised Audio Transformers (SAT) enable great success in many downstream speech applications like ASR, but how they work has not been widely explored yet.

Towards Robust Neural Vocoding for Speech Generation: A Survey

no code implementations5 Dec 2019 Po-chun Hsu, Chun-hsuan Wang, Andy T. Liu, Hung-Yi Lee

We found out that the speaker variety is much more important for achieving a universal vocoder than the language.

Speech Synthesis Voice Conversion

Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders

7 code implementations25 Oct 2019 Andy T. Liu, Shu-wen Yang, Po-Han Chi, Po-chun Hsu, Hung-Yi Lee

We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech.

Frame General Classification +3

Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion

1 code implementation28 May 2019 Andy T. Liu, Po-chun Hsu, Hung-Yi Lee

We found that the proposed encoding method offers automatic extraction of speech content from speaker style, and is sufficient to cover full linguistic content in a given language.

Voice Conversion

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