SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing

TL;DR

SpeechT5 introduces a unified encoder-decoder pre-training framework for speech and text, leveraging large-scale unlabeled data and cross-modal vector quantization to improve performance across diverse spoken language tasks.

Contribution

It presents a novel unified-modal SpeechT5 model with shared encoder-decoder and modal-specific nets, enabling cross-modal learning for speech and text.

Findings

Outperforms existing models on multiple spoken language tasks

Effective cross-modal alignment via vector quantization

Achieves state-of-the-art results in speech recognition and translation

Abstract

Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder. Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder. Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification. We release our code and model at https://github.com/microsoft/SpeechT5.