Unsupervised neural and Bayesian models for zero-resource speech processing
Table 5.5: A breakdown of the errors on Xitsonga for the speaker-dependent models in Table 5.3. The word boundary detection tolerance is 20 ms. The greedy one-to-one cluster mapping is used for error rate computations.
['Model', 'Embeds.', 'Word boundary (%) Prec.', 'Word boundary (%) Rec.', 'Word boundary (%)  [ITALIC] F', 'Errors (%) Sub.', 'Errors (%) Del.', 'Errors (%) Ins.', 'Errors (%) WER', 'Purity Avg.\xa0(%)']
SyllableBayesClust gives an upper-bound for word boundary recall since every syllable boundary is set as a word boundary. The low recall (28.9% and 24.8%) could potentially be improved by using a better syllabification method, but we decided to use the method as provided, and leave further investigation for future work. This is not surprising since every syllable is treated as a word, while most of the true Xitsonga words are multisyllabic. At the cost of more deletions and poorer word boundary detection, BayesSeg-MFCC and BayesSegMinDur-MFCC systematically reduces substitution and insertion errors, again resulting in better overall WER and average cluster purity. Where the cAE-based models on English2 performed more-or-less on par with their MFCC counterparts, on Xitsonga the cAE embeddings yield large improvements on some metrics: by switching to cAE embeddings, the WER of BayesSeg improves by 8.3% absolute, while average cluster purity is 13.6% better for BayesSegMinDur.