3 papers
eess.AS2026
Teaching the Teachers: Boosting unsupervised domain adaptation in speech recognition by ensemble update
Rehan Ahmad, Muhammad Umar Farooq, Qihang Feng +1
Speech recognition systems often struggle with data domains that have not been included in the training. To address this, unsupervised domain adaptation has been explored with ense…
eess.AS2024
Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training
Rehan Ahmad, Muhammad Umar Farooq, Thomas Hain
In Automatic Speech Recognition (ASR), teacher-student (T/S) training has shown to perform well for domain adaptation with small amount of training data. However, adaption without…
cs.CL2023
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition
Muhammad Umar Farooq, Rehan Ahmad, Thomas Hain
Student-teacher learning or knowledge distillation (KD) has been previously used to address data scarcity issue for training of speech recognition (ASR) systems. However, a limitat…