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- W4317438049 abstract "BERT-based re-ranking and dense retrieval (DR) systems have been shown to improve search effectiveness for spoken content retrieval (SCR). However, both methods can still show a reduction in effectiveness when using ASR transcripts in comparison to accurate manual transcripts. We find that a known-item search task on the How2 dataset of spoken instruction videos shows a reduction in mean reciprocal rank (MRR) scores of 10-14%. As a potential method to reduce this disparity, we investigate the use of semi-supervised ASR transcripts and N-best ASR transcripts to mitigate ASR errors for spoken search using BERT-based ranking. Semi-supervised ASR transcripts brought 2-5.5% MRR improvements over standard ASR transcripts and our N-best early fusion methods for BERT DR systems improved MRR by 3-4%. Combining semi-supervised transcripts with N-best early fusion for BERT DR reduced the MRR gap in search effectiveness between manual and ASR transcripts by more than 50% from 14.32% to 6.58%." @default.
- W4317438049 created "2023-01-19" @default.
- W4317438049 creator A5018481328 @default.
- W4317438049 creator A5060841056 @default.
- W4317438049 date "2023-01-15" @default.
- W4317438049 modified "2023-09-25" @default.
- W4317438049 title "Improving Noise Robustness for Spoken Content Retrieval using Semi-supervised ASR and N-best Transcripts for BERT-based Ranking Models" @default.
- W4317438049 doi "https://doi.org/10.48550/arxiv.2301.06056" @default.
- W4317438049 hasPublicationYear "2023" @default.
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