Unsupervised neural and Bayesian models for zero-resource speech processing
Table 3.1: Average precision (AP) on the test set using MFCCs, the UBM and partitioned UBM, the stacked and correspondence AEs trained on the 100k gold standard word pairs, and supervised NNs. Best performance when using weak top-down constraints is highlighted.
['Features', 'AP']
The partitioned UBM and the cAE were both trained on the gold standard 100k word-pair set. The optimal cAE network on the development set The second-to-last encoding layer was also used for obtaining representations from the stacked AE. In this case, the cAE is truly used as an unsupervised frame-level representation learning method. The first UTD run produced 25k word pairs at an accuracy of 46%, while the second (applied to additional unlabelled speech data) produced 80k pairs at 36%. Correspondence AEs were trained separately on the two sets of weak top-down constraints, with each optimized on the development data. In both cases, representations from the second-to-last encoding layer were again used. Both cAEs significantly outperform the MFCCs and stacked AE baselines by more than 57% relative in AP, coming to within 23% of the 10-hour supervised NN baseline.