Query-by-Example Search with Discriminative Neural Acoustic Word Embeddings
Table 2: Effect of number of permutations P on S-RAILS+NAWE performance on the development set, for signature length b=1024 and beamwidth B=2,000.
['[ITALIC] B', '[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']
Again in contrast to the S-RAILS system, our method responds strongly to increases in the number of permutations used. This is to be expected if the neural embeddings provide a better measure of speech segment distances, since the increased number of permutations helps provide a more exact estimate of the embedding distance. Further increasing the number of permutations may further improve these metrics, but this incurs a large cost in memory. To obtain higher precision systems, it is more important to use computational resources for increasing the number of permutations or using longer signatures. However, as would be expected, the higher beamwidths help to significantly improve the FOM score, a metric concerned primarily with recall.