M2H-GAN: A GAN-based Mapping from Machine to Human Transcripts for Speech Understanding
Table 3: Accuracies obtained by proposed generative models, compared to previous works on the DECODA corpus. “Real Test” stands for the performances observed on the test set w.r.t to the validation set, while “Max Test” are the best results obtained. Results are averaged over 10 runs. The standard deviation is computed over these runs and concerns the “Real Test” performances
['[BOLD] Models', '[BOLD] Data', '[BOLD] Dev.', '[BOLD] Real Test', '[BOLD] Max Test', '[BOLD] Std. Dev.']
Indeed, encoder and decoder are trained jointly to minimize the reconstruction error, while the generator and discriminator are trained on different objectives impacting on each other. M2H-GAN also give better results than the recent quaternion-valued denoising auto-encoder (QDAE), despite the fact that the QDAE is based on a better document representation and a specific segmentation with the quaternion algebra.