Improving Noise Robustness for Spoken Content Retrieval using Semi-supervised ASR and N-best Transcripts for BERT-based Ranking Models
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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 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 standard ASR transcripts and our N-best early fusion methods for BERT DR systems improved MRR by 3-4 early fusion for BERT DR reduced the MRR gap in search effectiveness between manual and ASR transcripts by more than 50
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