Unsupervised neural and Bayesian models for zero-resource speech processing
Table 4.1: Performance of the unconstrained segmental Bayesian model on TIDigits1 over iterations in which the reference set is refined.
['Metric', '1', '2', '3', '4', '5']
Random word boundary initialization is used throughout. Unconstrained modelling represents the most realistic setting where vocabulary size is not known upfront. Standard deviations are less than 0.3% absolute for all metrics. Despite being allowed to discover many more clusters (up to 100) than the true number of word types (11), the model achieves a WER of 35.4% in the first iteration, which improves to around 21% in iterations 3 and 4. This shows that to achieve good performance, it is essential to use top-down refinement for the acoustic word embeddings used here. Error rate increases slightly in iteration 5. Cluster purity over all iterations is above 86.5%, which is higher than the scores of around 85% reported by Sun and Van hamme Word boundary F-scores are around 70% over all iterations. As mentioned, the Bayesian GMM is biased not to use all of its 100 components. Despite this, none of the models empty out any of their components.