Główne pojęcia
The author explores the effectiveness of adapters in noise-robust ASR, focusing on insertion points, training data impact, and synergy with speech enhancement systems.
Streszczenie
The study investigates adapter-based ASR adaptation in noisy environments using the CHiME-4 dataset. Results show superior effectiveness when inserting the adapter in the shallow layer. Real data proves more effective than simulated data for training. Adapters integrated into speech enhancement systems yield substantial improvements.
Statystyki
The relative improvement was 4% and 7% for development and evaluation sets when incorporating simulated data during training.
The relative improvements for experiments 17–18, 20–21, 23–24 were 6%, 16%, and 25% in development sets.
The relative improvements for experiments 18–19, 21–22, 24–25 were 2% (development sets), 9% (development sets), and 12% (development sets).
Experiments showed consistent performance across different embedding dimensions of the adapter.
Cytaty
"Incorporating adapters into ASR models is a prevalent practice for tasks like accent ASR, children ASR, and multi-lingual ASR."
"Real data yields better adaptation performance when using the same amount of data than simulated data."
"The experimental results demonstrate that incorporating adapters in the shallow layer yields more effectiveness compared to the deep layer."