Improving End-to-End Bangla Speech Recognition with Semi-supervised Training
Nafis Sadeq, Nafis Tahmid Chowdhury, Farhan Tanvir Utshaw, Shafayat Ahmed, Muhammad Abdullah Adnan · 2020
Automatic speech recognition systems usually require large annotated speech corpus for training.The manual annotation of a large corpus is very difficult.It can be very helpful to use unsupervised and semi-supervised learning methods in addition to supervised learning.In this work, we focus on using a semi-supervised training approach for Bangla Speech Recognition that can exploit large unpaired audio and text data.We encode speech and text data in an intermediate domain and propose a novel loss function based on the global encoding distance between encoded data to guide the semisupervised training.Our proposed method reduces the Word Error Rate (WER) of the system from 37% to 31.9%.