A Study of Enhancement, Augmentation, and Autoencoder Methodsfor Domain Adaptation in Distant Speech Recognition
Table 4: WERs (%) for models trained on hidden vectors produced by an FHVAE, where the rows with μ1 use the mean of shared distribution and the rows with logσ1 use the log variance of the shared distribution as features.
['train', 'target', '[EMPTY]']
After the FAVAE is trained, we use the encoder for the shared distribution to produce features. A feature vector is generated at each time point by taking a 20-frame segment centered at the current time point and feeding it forward into the encoder. The hidden vectors are then normalized by subtracting the mean and dividing by the standard deviation computed over the training set. TDNNs are trained on the produced feature vectors with the same training procedure as in previous sections. Since the distribution is modeled as a Gaussian, we use the Gaussian mean vectors and have the option to include the log-variance vectors as features. While there is a small amount of degradation in the IHM domain, we see an improvement from 70.3% to 61.8% in the SDM domain. This suggests that the SDM features produced by the FHVAE are closer to IHM in the latent space. The improvement is even larger than data augmentation with simulated reverberation. However, we find that including the log-variance as features might not help adapting to the target domain. This needs further investigation.