Unsupervised neural and Bayesian models for zero-resource speech processing
Table 4.2: WER (%) on TIDigits1 of the unsupervised discrete HMM system of Walter et al. (2013) and the segmental Bayesian model.
['Model', 'Constrained', 'Random init.', 'UTD init.']
Constrained model evaluation and comparison To compare with the discrete HMM-based system of Walter et al. Under random initialization, the constrained segmental Bayesian model performs 12.7% absolute better than the discrete HMM. When using UTD for initialization, the discrete HMM does better by 1.3% absolute. Despite only mapping eleven out of 100 clusters to true labels, this unconstrained model still yields 10.6% absolute lower WER than the randomly-initialized discrete HMM with the correct number of clusters. By comparing rows two and three, we observe that there is only a 2.1% absolute gain in WER by constraining the Bayesian model to a stricter number of types.