A Transformer-Based End-to-End Automatic Speech Recognition Algorithm

Abstract

End-to-End (E2E) automatic speech recognition (ASR) becomes popular recent years and has been widely used in many applications. However, current ASR algorithms are usually less effective when applied in specific applications with terminologies such as medical and economic fields. To address this issue, we propose a powerful Transformer based ASR decoding method for beam searching, called soft beam pruning algorithm (SBPA). SBPA can dynamically adjust the width of beam search. Meanwhile, a prefix module (PM) is added to access the contextual information and avoid removing professional words in the beam search. Combining SBPA and PM, the proposed ASR can achieve promising recognition performance on professional terminologies. To verify the effectiveness, experiments are conducted on real-world conversation data with medical terminology. It is shown that the proposed ASR achieved significant performance on both professional and regular words.