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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.AS2025
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning
Prabhat Pandey, Rupak Vignesh Swaminathan, K V Vijay Girish +4
We introduce SIFT (Speech Instruction Fine-Tuning), a 50M-example dataset designed for instruction fine-tuning and pre-training of speech-text large language models (LLMs). SIFT-50…
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…