1.1k citations · 2.1k across the 63 of their papers we have counts for
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How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario
Shih-Heng Wang, Zih-Ching Chen, Jiatong Shi +6
The utilization of speech Self-Supervised Learning (SSL) models achieves impressive performance on Automatic Speech Recognition (ASR). However, in low-resource language ASR, they e…
Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting
Zhenyu Wang, Li Wan, Biqiao Zhang +5
A keyword spotting (KWS) engine that is continuously running on device is exposed to various speech signals that are usually unseen before. It is a challenging problem to build a s…
FLAP: Fast Language-Audio Pre-training
Ching-Feng Yeh, Po-Yao Huang, Vasu Sharma +2
We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, c…
Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond
Jiatong Shi, William Chen, Dan Berrebbi +10
The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual s…
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark
Jiatong Shi, Dan Berrebbi, William Chen +8
Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks…