Transformer-based Model for ASR N-Best Rescoring and Rewriting

• URL: http://arxiv.org/abs/2406.08207v1
• Date: Wed, 12 Jun 2024 13:39:44 GMT
• Title: Transformer-based Model for ASR N-Best Rescoring and Rewriting
• Authors: Iwen E. Kang, Christophe Van Gysel, Man-Hung Siu,
• Abstract summary: We propose a novel Transformer based model capable of rescoring and rewriting, by exploring full context of the N-best hypotheses in parallel. 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.
• Score: 4.906869033128613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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