Unsupervised neural and Bayesian models for zero-resource speech processing
Table 5.6: Performance on the three datasets for speaker-independent models.
['Model', 'Embeds.', 'One-to-one WER (%) English1', 'One-to-one WER (%) English2', 'One-to-one WER (%) Xitsonga', 'Many-to-one WERm (%) English1', 'Many-to-one WERm (%) English2', 'Many-to-one WERm (%) Xitsonga']
As in the speaker-dependent case, BayesSegMinDur is the best performing MFCC system, followed by BayesSeg, and SyllableBayesClust performs worst. In the speaker-dependent experiments, some MFCC-based models slightly outperformed their cAE counterparts. Here, however, the WERs of cAE models are identical or improved in all cases; for Xitsonga in particular, improvements are obtained by using cAE features in both BayesSeg (improvement of 26.3% absolute in WER) and BayesSegMinDur (7.4%). The cAE-based BayesSegMinDur model is the only speaker-independent Xitsonga model with a WER less than 100%. Again, by allowing more than one cluster to be mapped to the same true word type, WERm scores are lower than WER. Although the cAE-based models do not result in better scores on the English data, the cAE features yield large improvements on Xitsonga. Word boundary scores and substitution, deletion and insertion errors (not shown) follow a similar pattern to that of the speaker-dependent models.