Learned in Speech Recognition: Contextual Acoustic Word Embeddings
Table 1: Comparing three methods to obtain acoustic word embeddings from an A2W model: unweighted average (U-AVG), weighted average (CAWE-W) and maximum attention (CAWE-M).
['Dataset', 'Switchboard U-AVG', 'Switchboard CAWE-W', 'Switchboard CAWE-M', 'How2 U-AVG', 'How2 CAWE-W', 'How2 CAWE-M']
Discussion: We observe that CAWE-W usually performs worse than CAWE-M which could be attributed to a noisy estimation of the word embeddings on the account of taking even the less confident attention scores while constructing the embedding. In contrast, CAWE-M is constructed using the most confident attention score obtained over all the occurrences of the acoustic frames corresponding to a particular word. We also observe that U-AVG performs worse than CAWE-W on STS and SICK-R tasks since it is constructed using an even noisier process in which all encoder hidden representations are weighted equally irrespective of their attention scores.