Semi-Supervised Phoneme Recognition with Recurrent Ladder Networks
Table 1: Best results in phoneme error rate (PER), achieved by the proposed RLN modelling options: No decoder (ND, baseline), recurrent decoder (RD), feedforward decoder (FFD), feedforward noise (FFN), and recurrent noise (RN). †: linear interpolation between 10% and 30% labels. ††: Graves et al. [7] have shown significantly improved results with more parameters (17.7% PER, 4.3m param.).
['labels', 'ND-FFN', 'RD-FFN', 'FFD-FFN', 'ND-RN', 'RD-RN', 'FFD-RN', 'SSSAE ', 'BLSTM ']
An overview of our results can be seen in Fig. On average, the RD models perform better than the FFD models for most σ, more so with fewer labels, suggesting that the recurrent decoder is better at filtering noise. This also explains why the RD models work better with higher σ compared to FFD.