7 papers · 1 filter
QuaSR: Quality-Aware Sample Reweighting for Pacific Indigenous Speech Recognition
Yishun Li, Yang Xiao, Gongping Huang +3
Training automatic speech recognition (ASR) models for low-resource languages is challenging due to limited data and highly variable supervision quality. In particular, Pacific Ind…
Continual Adaptation for Pacific Indigenous Speech Recognition
Yang Xiao, Aso Mahmudi, Nick Thieberger +3
Speech foundation models struggle with low-resource Pacific Indigenous languages because of severe data scarcity. Furthermore, full fine-tuning risks catastrophic forgetting. To ad…
ImKWS: Test-Time Adaptation for Keyword Spotting with Class Imbalance
Hanyu Ding, Yang Xiao, Jiaheng Dong +1
Keyword spotting (KWS) identifies words for voice assistants, but environmental noise frequently reduces accuracy. Standard adaptation fixes this issue and strictly requires origin…
Activation Steering for Accent Adaptation in Large Audio Language Models
Jinuo Sun, Yang Xiao, Sung Kyun Chung +4
Accent variability remains a major source of errors in automatic speech recognition, yet most adaptation methods rely on parameter fine-tuning without understanding where accent in…
AdaKWS: Towards Robust Keyword Spotting with Test-Time Adaptation
Yang Xiao, Tianyi Peng, Yanghao Zhou +1
Spoken keyword spotting (KWS) aims to identify keywords in audio for wide applications, especially on edge devices. Current small-footprint KWS systems focus on efficient model des…
AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting
Yang Xiao, Tianyi Peng, Rohan Kumar Das +2
Keyword spotting (KWS) offers a vital mechanism to identify spoken commands in voice-enabled systems, where user demands often shift, requiring models to learn new keywords continu…
Dark Experience for Incremental Keyword Spotting
Tianyi Peng, Yang Xiao
Spoken keyword spotting (KWS) is crucial for identifying keywords within audio inputs and is widely used in applications like Apple Siri and Google Home, particularly on edge devic…