Unified Speech-Text Pre-training for Speech Translation and Recognition
It addresses speech processing tasks by integrating linguistic text information into speech pre-training, but it is incremental as it builds on existing cross-modality learning methods.
The paper tackles the problem of speech translation and recognition by jointly pre-training speech and text in an encoder-decoder framework, achieving a 1.7 to 2.3 BLEU improvement on MuST-C and comparable WERs to wav2vec 2.0 on Librispeech.
We describe a method to jointly pre-train speech and text in an encoder-decoder modeling framework for speech translation and recognition. The proposed method incorporates four self-supervised and supervised subtasks for cross modality learning. A self-supervised speech subtask leverages unlabelled speech data, and a (self-)supervised text to text subtask makes use of abundant text training data. Two auxiliary supervised speech tasks are included to unify speech and text modeling space. Our contribution lies in integrating linguistic information from the text corpus into the speech pre-training. Detailed analysis reveals learning interference among subtasks. Two pre-training configurations for speech translation and recognition, respectively, are presented to alleviate subtask interference. Our experiments show the proposed method can effectively fuse speech and text information into one model. It achieves between 1.7 and 2.3 BLEU improvement above the state of the art on the MuST-C speech translation dataset and comparable WERs to wav2vec 2.0 on the Librispeech speech recognition task.