Self-supervised pre-training has been successful in both text and speech
processing. Speech and text offer different but complementary information. The
question is whether we are able to perform a speech-text joint pre-training on
unpaired speech and text. In this paper, we take the idea of self-supervised
pre-training one step further and propose token2vec, a novel joint pre-training
framework for unpaired speech and text based on discrete representations of
speech. Firstly, due to the distinct characteristics between speech and text
modalities, where speech is continuous while text is discrete, we first
discretize speech into a sequence of discrete speech tokens to solve the
modality mismatch problem. Secondly, to solve the length mismatch problem,
where the speech sequence is usually much longer than text sequence, we convert
the words of text into phoneme sequences and randomly repeat each phoneme in
the sequences. Finally, we feed the discrete speech and text tokens into a
modality-agnostic Transformer encoder and pre-train with token-level masking
language modeling (tMLM). Experiments show that token2vec is significantly
superior to various speech-only pre-training baselines, with up to 17.7%
relative WER reduction. Token2vec model is also validated on a non-ASR task,
i.e., spoken intent classification, and shows good transferability.

本文提出了一种新型的基于离散语音表示的非配对语音和文本联合预训练框架，即 Token2Vec，通过模态不可知的 Transformer 编码器和令牌级掩码语言建模（tMLM）进行预训练，在非 ASR 任务上也表现出很好的可转移性，相对于各种仅语音预训练的基线，Token2Vec 的性能显著提高，最高相对 WER 降低了 17.7%。