4 citations · 4 across the 2 of their papers we have counts for
5 papers
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation
Fengpeng Yue, Yan Deng, Lei He +1
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 e…
Auto-KWS 2021 Challenge: Task, Datasets, and Baselines
Jingsong Wang, Yuxuan He, Chunyu Zhao +5
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. Compared…
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification
Jingsong Wang, Tom Ko, Zhen Xu +4
The AutoSpeech challenge calls for automated machine learning (AutoML) solutions to automate the process of applying machine learning to speech processing tasks. These tasks, which…
MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization
Yangbin Chen, Yun Ma, Tom Ko +2
Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known a…
An Investigation of Few-Shot Learning in Spoken Term Classification
Yangbin Chen, Tom Ko, Lifeng Shang +3
In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few…