Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings
Table 4: Comparison of QbE system performance on the evaluation set.
['System', '[BOLD] Median Example  [BOLD] FOM', '[BOLD] Median Example  [BOLD] OTWV', '[BOLD] Median Example  [BOLD] P@10', '[BOLD] Best Example  [BOLD] FOM', '[BOLD] Best Example  [BOLD] OTWV', '[BOLD] Best Example  [BOLD] P@10', '[BOLD] Query time (s)']
We find that our approach improves significantly over both RAILS and S-RAILS in terms of all performance metrics at this operating point. The biggest gains from S-RAILS+NAWE are seen in the Median Example results, where there is a relative improvement over S-RAILS of more than 55% across all measures. In terms of FOM and OTWV, we see relative improvements of over 40% in the Best Example case. Although the baselines obtain good P@10, we still find large improvements in this measure as well, from 87.1% to 95.1%.