Large-scale ASR Domain Adaptation using Self- and Semi-supervised Learning

2021/10/01 by Dongseong Hwang, Hwang, Dongseong, Ananya Misra +17 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Audio and Speech Processing (eess.AS) #Cancer-related molecular mechanisms research #Computation and Language (cs.CL) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Sound (cs.SD) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2110.00165

openalex publication_date 2021/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance. However, the approach mostly focus on in-domain performance for public datasets. In this study, we utilize the combination of self- and semi-supervised learning methods to solve unseen domain adaptation problem in a large-scale production setting for online ASR model. This approach demonstrates that using the source domain data with a small fraction of the target domain data (3%) can recover the performance gap compared to a full data baseline: relative 13.5% WER improvement for target domain data.

Cited by

• BEST-RQ-Based Self-Supervised Learning for Whisper Domain Adaptation
• Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
• LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models Using in-the-wild Data
• Customizing Speech Recognition Model with Large Language Model Feedback

Related

• Better Semi-supervised Learning for Multi-domain ASR Through Incremental Retraining and Data Filtering
• Data Techniques For Online End-to-end Speech Recognition
• MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR
• DRAFT: A Novel Framework to Reduce Domain Shifting in Self-supervised Learning and Its Application to Children's ASR
• Efficient Data Selection for Domain Adaptation of ASR Using Pseudo-Labels and Multi-Stage Filtering