Improving End-To-End Speech Translation Model with Bert-Based Contextual Information
Jeong‐Uk Bang, Minkyu Lee, Seung Yun, Sanghun Kim · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
This paper proposes an end-to-end speech translation system that utilizes contextual information. Contextual information helps clarify the meaning of the utterances. However, conventional end-to-end speech translation (E2E-ST) is primarily designed to handle single-utterance. Thus, we introduce a context encoder that extracts contextual information from previous translation results. Here, the context encoder obtains high-quality contextual information by adopting the BERT model. Then, we combine it with speech information extracted from speech signals to generate translation results. On the widely used TED-based speech translation corpus, we show that the results of the contextual E2E-ST model are significantly better than those of the single utterance-based E2E-ST model. Furthermore, we demonstrate that contextual information contributes to the processing of unclearly spoken utterances as well as ambiguity caused by pronouns and homophones.