A Study of Enhancement, Augmentation, and Autoencoder Methodsfor Domain Adaptation in Distant Speech Recognition
Table 1: WERs (%) for models trained and tested on various domains.
['train', 'target', '[EMPTY]']
Since we are interested in adapting models trained on close-talking speech to distant speech, we train two TDNNs, one on IHM and one on SDM, and test them on utterances from both IHM and SDM. We use stochastic gradient descent (SGD) with a fixed step size 0.025 and a mini-batch size of 1 utterance to optimize the cross entropy for 20 epochs. Gradients are clipped to norm 5. We choose the best performing model from the 20 epochs based on the frame error rates, and train it for another 5 epochs with step size 0.025×0.75 decayed by 0.75 after each epoch. The WER increases from 27.4% to 70.3% when using a close-talking model on distant speech. For consistency, we use IHM alignments for the rest of the experiments. For multi-condition training, we train and tune TDNNs on both IHM and SDM combined. The training procedure remains the same except that we use a smaller initial step size 0.01. The TDNN is able to match the results on both domains.