Fused acoustic and text encoding for multimodal bilingual pretraining and speech translation

Abstract

Representation learning for text and speech has improved many language-related tasks. However, existing methods only learn from one input modality, while a unified representation for both speech and text is needed for tasks such as end-to-end speech translation. Consequently, these methods cannot exploit various large-scale text and speech data and their performance is limited by the scarcity of parallel speech translation data. To address these problems, embodiments of a fused acoustic and text masked language model (FAT-MLM) are disclosed. FAT-MLM embodiments jointly learn a unified representation for both acoustic and text input from various types of corpora including parallel data for speech recognition and machine translation, and pure speech and text data. Within this cross-modal representation learning framework, an end-to-end model is further presented for fused acoustic and text speech translation. Experiments show that by fine-tuning from FAT-MLM, the speech translation model embodiments substantially improve translation quality.