Transformer-based Model for ASR N-Best Rescoring and Rewriting
<|reference_start|>Transformer-based Model for ASR N-Best Rescoring and Rewriting: Voice assistants increasingly use on-device Automatic Speech Recognition (ASR) to ensure speed and privacy. However, due to resource constraints on the device, queries pertaining to complex information domains often require further processing by a search engine. For such applications, we propose a novel Transformer based model capable of rescoring and rewriting, by exploring full context of the N-best hypotheses in parallel. We also propose a new discriminative sequence training objective that can work well for both rescore and rewrite tasks. We show that our Rescore+Rewrite model outperforms the Rescore-only baseline, and achieves up to an average 8.6% relative Word Error Rate (WER) reduction over the ASR system by itself.<|reference_end|>
@article{kang2024transformer-based,
    title={Transformer-based Model for ASR N-Best Rescoring and Rewriting},
    author={Iwen E. Kang and Christophe Van Gysel and Man-Hung Siu},
    journal={arXiv preprint arXiv:2406.08207},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08207},
    primaryClass={eess.AS cs.CL cs.LG cs.SD}
}