Unsupervised neural and Bayesian models for zero-resource speech processing
Table 5.7: Average speaker-independent cluster (clust.), speaker (spk.), and gender (gndr) purity for BayesSegMinDur on the three datasets.
['Embeds.', 'English1 (%) Clust.', 'English1 (%) Spk.', 'English1 (%) Gndr', 'English2 (%) Clust.', 'English2 (%) Spk.', 'English2 (%) Gndr', 'Xitsonga (%) Clust.', 'Xitsonga (%) Spk.', 'Xitsonga (%) Gndr']
Average cluster purity is as defined before. Average speaker purity is similarly defined, but instead of considering the mapped ground truth label of a segmented token, it considers the speaker who produced it: speaker purity is 100% if every cluster contains tokens from a single speaker, while it is 1/12=8.3% if all clusters are completely speaker balanced for the English sets and 1/24=4.2% for Xitsonga. Average gender purity is similarly defined: it is 100% if every cluster contains tokens from a single gender, while 1/2=50% indicates a perfectly gender-balanced cluster. Ideally, a speaker-independent system should have high cluster purity and low speaker and gender purities.