This paper presents the Automatic Speech Recognition (ASR) system we developed for Odia. Odia is the primary language of the Indian state of Odisha, and lacks sufficient annotated speech corpora. However, some other languages have larger publicly available speech resources. Therefore, we employed Transfer Learning for the development. First, we build monolingual pre-trained models using Bengali, Hindi, and English resources. Then, we used the pre-trained models along with the Odia data to develop the ASR model using a Residual Refinement Learning (RRL) network. This transfer learning model performs better than the baseline model. Certain multilingual pre-trained models, such as Whisper-small and Wav2Vec2.0 XLSR-53, have been quite popular in various speech processing tasks. We also employed those models in the Odia ASR task and found that they improve the performance. Furthermore, we propose a hybrid transfer learning technique where two pre-trained models are combined. There, the Whisper-small and Wav2Vec2.0 are combined with the RRL framework. The proposed hybrid transfer learning model outperformed all the previous models. The final model achieved a word error rate (WER) of 1.15 and a character error rate (CER) of 0.14, which is significantly better than the existing Odia ASR systems. The superiority of the proposed model is also tested by implementing several systems and datasets on other Indian languages on a unified platform.
Majhi et al. (Tue,) studied this question.