Unsupervised neural and Bayesian models for zero-resource speech processing
Table 3.2: Average precision (AP) on the test set using the partitioned UBM (Jansen et al., 2013b) and correspondence AEs when varying the number of gold standard word pairs N, with F the resulting number of frame pairs.
['[ITALIC] N', '[ITALIC] F', 'Partitioned UBM AP', 'Correspondence AE AP']
For every set, the cAE was optimized on the development data. In all cases the cAE outperforms the partitioned UBM and the baseline MFCCs. With as few as 1k word pairs, the cAE gives the same performance as the partitioned UBM trained with all pairs. This is attributed to the lower word-pair accuracy of the former, and shows that there is a trade-off between UTD accuracy and the number of pairs produced. A finer-grained investigation of the trade-off between the number of word pairs and accuracy, which can be varied by searching more data or by adjusting the search threshold, could be the focus of future work.