Unsupervised neural and Bayesian models for zero-resource speech processing
Table 5.4: A breakdown of the errors on English2 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. However, this is accompanied by large reductions in substitution and insertion error rates, resulting in overall WER improvements and more accurate clusters when boundaries are inferred (45.1% purity, BayesSeg-MFCC) rather than using fixed syllable boundaries (42%, SyllableBayesClust), with further improvements when not allowing short word candidates (56%, BayesSegMinDur-MFCC).