Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation
Table 4: Per–genre WER for all of the models
['Adaptation', 'WER (%) Advice', 'WER (%) Child.', 'WER (%) Comedy', 'WER (%) Compet.', 'WER (%) Docum.', 'WER (%) Drama', 'WER (%) Even.', 'WER (%) News', 'WER (%)  [BOLD] Overall']
LDaT training reduces the WER from 33.3% to 30.6%, which is even better than speaker adapted DNN (31.4%). Combining speaker adaptation and domain adaptation (SAT+LDaT, linear input transformation for the speaker and bias adaptation for the latent domain) yields 28.9%, which is 13% relative WER reduction compared to the baseline DNN model and 8% relative improvement over the speaker adapted DNN. This also suggests that LDA inferred domains were not speaker clusters (since combining two adaptations still improves the performance). Because of the diverse nature of the data used, WER differs a lot across genres. Namely comedy and drama had the highest errors (43.8% and 45.0% respectively with LDaT+SAT models) showing the difficult nature of these genres. On the other hand, news had the lowest WER (14.3%).