Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Table 3: Comparison of word error rate of each method on continuous speech recognition. In column ”No LM”, no language model was used.
['Method', 'No LM', 'Bigram']
The performance improvements over the phone based HMM-DNN method in with and without the language model scenarios were 10.68% and 5.11%, respectively. The results suggest that the proposed data-driven dictionary and the AAE models are also useful for CSR and a more accurate representation of speech signals can be learned automatically. We observed that all 384 AAEs were actually used in the trained dictionary, and the dictionary tend to assign 39% more HMM states on average to each word as compare with the TIMIT phonetic dictionary. This means that in AAEs, the stay-in-state probability is smaller resulting in more frequent state transitions. This suggests that by using AAEs, the acoustic space was modeled at a higher resolution. This consequently increased the precision of the word pronunciations.