Semi-Supervised Learning with Sparse Autoencodersin Phone Classification
Table 2: Accuracy rates (%) for frame-based phoneme classification on TIMIT for the baseline (NN), the four different algorithms in GBL-SSL [7] and our model, SSSAE
['Comparison with other methods', 'Comparison with other methods', 'Comparison with other methods 10% labelled', 'Comparison with other methods 30% labelled']
We observe that our system performs better than all the techniques mentioned except the Prior Regularised Measure Propagation (pMP) algorithm.