Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Table 2: Word error rates in % of AAE based recognizers with different number of AAEs and GMM mixture. The best performance for each number of AAE is plotted in Figure 2.
['# of AAE', '# of mixture 16', '# of mixture 32', '# of mixture 64', '# of mixture 128']
Our second experiment focused on the effects of the number of AAEs, i.e. N. We trained the dictionary and AAE models with N=64,128,192,256,320,384,448. For DNN based AAE models, the best result are obtained with 384 AAEs in contrast to with 320 AAEs for the GMM based models. Interestingly, the optimal number of AAE states is far higher than the number of states of the phone models (61 phonemes × 3 states = 183 states). This is an indication that the proposed data-driven approach to jointly generate the sub-word units and dictionary models the acoustic space more precisely than the linguistically motivated phonetic units and the manually designed dictionary. It is also worthwhile to mention that the optimal number of DNN based AAE models was higher than that of GMM based models. This is perhaps due to the fact that the DNN was trained discriminatively, allowing to efficiently model the interaction between higher number of AAEs.