Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Table 1: Comparison of word error rates of each method on 339 words isolated word recognition (%). Baseline phone models are trained by using the TIMIT dictionary.
['Method', 'WER']
The results show that the proposed data-driven method clearly outperforms the baseline methods. The proposed AAE-DNN method achieved 10.3% and 2.4% improvement over GMM and DNN based phonetic acoustic models, respectively. This suggests that a more accurate dictionary and better acoustic models can be obtained directly from training data without any human expertise. Moreover, AAE-DNN method improves the performance by 3.2% over the AAE-GMM method. This indicates that the DNN was successfully trained in the semi-supervised manner and the final model could effectively use the its expressive modeling power.