Speechformer: Reducing Information Loss in Direct Speech Translation
Journal: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Year: 2021

Transformer-based models have gained increasing popularity achieving state-of-the-art performance in many research fields including speech translation. However, Transformer's quadratic complexity with respect to the input sequence length prevents its adoption as is with audio signals, which are typically represented by long sequences. Current solutions resort to an initial sub-optimal compression based on a fixed sampling of raw audio features. Therefore, potentially useful linguistic informatio