A modified WaveNet for Filipino automatic speech recognition - Martinez, Miguel Gregorio Felipe C.
In this study we explore the feasibility of using a modified WaveNet architecture for Automatic Speech Recognition (ASR) with the Filipino language. The ISIP corpus was used as a source of utterances. Two different models were trained. One used a subset of the training set that excluded prompts consisting solely of proper nouns. The other used the entire training set. Connectionist Temporal Classification (CTC) loss was used to evaluate the performance of the models. The two models had similar CTC losses across all sets, and were two orders of magnitude higher on the validation and test sets than on the training set. The models showed poor generalization; both made inaccurate predictions on utterances not from the ISI corpus. The results indicate overfitting had occurred. It is suggested that either data augmentation or integration of a language model be performed in order to improve the performance of the models.