Search Results for author: Tom Ko

Found 14 papers, 7 papers with code

GigaST: A 10,000-hour Pseudo Speech Translation Corpus

no code implementations8 Apr 2022 Rong Ye, Chengqi Zhao, Tom Ko, Chutong Meng, Tao Wang, Mingxuan Wang, Jun Cao

The training set is translated by a strong machine translation system and the test set is translated by human.

Machine Translation Translation

LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

1 code implementation29 Mar 2022 Rui Wang, Qibing Bai, Junyi Ao, Long Zhou, Zhixiang Xiong, Zhihua Wei, Yu Zhang, Tom Ko, Haizhou Li

LightHuBERT outperforms the original HuBERT on ASR and five SUPERB tasks with the HuBERT size, achieves comparable performance to the teacher model in most tasks with a reduction of 29% parameters, and obtains a $3. 5\times$ compression ratio in three SUPERB tasks, e. g., automatic speaker verification, keyword spotting, and intent classification, with a slight accuracy loss.

Automatic Speech Recognition Intent Classification +3

SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

no code implementations ACL 2022 Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.

Automatic Speech Recognition Quantization +5

Multi-View Self-Attention Based Transformer for Speaker Recognition

no code implementations11 Oct 2021 Rui Wang, Junyi Ao, Long Zhou, Shujie Liu, Zhihua Wei, Tom Ko, Qing Li, Yu Zhang

In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.

Speaker Recognition

CL4AC: A Contrastive Loss for Audio Captioning

2 code implementations21 Jul 2021 Xubo Liu, Qiushi Huang, Xinhao Mei, Tom Ko, H Lilian Tang, Mark D. Plumbley, Wenwu Wang

Automated Audio captioning (AAC) is a cross-modal translation task that aims to use natural language to describe the content of an audio clip.

Audio captioning Translation

Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation

no code implementations8 Apr 2021 Fengpeng Yue, Yan Deng, Lei He, Tom Ko

Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech recognition (ASR) and text-to-speech (TTS) into one circle for joint training, has been proven to be effective in data augmentation by leveraging large amounts of unpaired data.

Automatic Speech Recognition Data Augmentation +1

Auto-KWS 2021 Challenge: Task, Datasets, and Baselines

1 code implementation31 Mar 2021 Jingsong Wang, Yuxuan He, Chunyu Zhao, Qijie Shao, Wei-Wei Tu, Tom Ko, Hung-Yi Lee, Lei Xie

Auto-KWS 2021 challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to a customized keyword spotting task.

AutoML Keyword Spotting

AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification

no code implementations25 Oct 2020 Jingsong Wang, Tom Ko, Zhen Xu, Xiawei Guo, Souxiang Liu, Wei-Wei Tu, Lei Xie

The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks.

AutoML General Classification

MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization

no code implementations29 Sep 2020 Yangbin Chen, Yun Ma, Tom Ko, Jian-Ping Wang, Qing Li

MetaMix can be integrated with any of the MAML-based algorithms and learn the decision boundaries generalizing better to new tasks.

Few-Shot Learning Transfer Learning

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