# SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training

arXiv: https://arxiv.org/abs/2210.03730 · HTML: https://mlsanity.com/paper/2210.03730/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 54 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training](https://mlsanity.com/paper/2108.06209/) (2021)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition](https://mlsanity.com/paper/2010.10504/) (2020)
- [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://mlsanity.com/paper/2110.07205/) (2021)
- [On Using Monolingual Corpora in Neural Machine Translation](https://mlsanity.com/paper/1503.03535/) (2015)
- [Direct speech-to-speech translation with discrete units](https://mlsanity.com/paper/2107.05604/) (2021)
- [Textless Speech-to-Speech Translation on Real Data](https://mlsanity.com/paper/2112.08352/) (2021)
- [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://mlsanity.com/paper/1911.08460/) (2019)
- [mSLAM: Massively multilingual joint pre-training for speech and text](https://mlsanity.com/paper/2202.01374/) (2022)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [MAESTRO: Matched Speech Text Representations through Modality Matching](https://mlsanity.com/paper/2204.03409/) (2022)
- [SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training](https://mlsanity.com/paper/2110.10329/) (2021)
- [Unified Speech-Text Pre-training for Speech Translation and Recognition](https://mlsanity.com/paper/2204.05409/) (2022)
- [Cross-modal Contrastive Learning for Speech Translation](https://mlsanity.com/paper/2205.02444/) (2022)
- [STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation](https://mlsanity.com/paper/2203.10426/) (2022)
- [SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data](https://mlsanity.com/paper/2209.15329/) (2022)

## Built on by (most cited first, 30 of 40)

- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling](https://mlsanity.com/paper/2303.03926/) (2023)
- [ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities](https://mlsanity.com/paper/2305.11172/) (2023)
- [VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning](https://mlsanity.com/paper/2211.11275/) (2022)
- [Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing](https://mlsanity.com/paper/2402.15151/) (2024)
- [AV-data2vec: Self-supervised Learning of Audio-Visual Speech Representations with Contextualized Target Representations](https://mlsanity.com/paper/2302.06419/) (2023)
- [CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation](https://mlsanity.com/paper/2305.14635/) (2023)
- [WACO: Word-Aligned Contrastive Learning for Speech Translation](https://mlsanity.com/paper/2212.09359/) (2022)
- [DUB: Discrete Unit Back-translation for Speech Translation](https://mlsanity.com/paper/2305.11411/) (2023)
- [Understanding and Bridging the Modality Gap for Speech Translation](https://mlsanity.com/paper/2305.08706/) (2023)
- [CopyNE: Better Contextual ASR by Copying Named Entities](https://mlsanity.com/paper/2305.12839/) (2023)
- [Pushing the Limits of Zero-shot End-to-End Speech Translation](https://mlsanity.com/paper/2402.10422/) (2024)
- [Textless Unit-to-Unit training for Many-to-Many Multilingual Speech-to-Speech Translation](https://mlsanity.com/paper/2308.01831/) (2023)
- [TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation](https://mlsanity.com/paper/2405.17809/) (2024)
- [Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation](https://mlsanity.com/paper/2210.17027/) (2022)
- [MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition](https://mlsanity.com/paper/2212.00500/) (2022)
- [Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data](https://mlsanity.com/paper/2212.01778/) (2022)
- [Back Translation for Speech-to-text Translation Without Transcripts](https://mlsanity.com/paper/2305.08709/) (2023)
- [ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2305.14838/) (2023)
- [Tuning Large language model for End-to-end Speech Translation](https://mlsanity.com/paper/2310.02050/) (2023)
- [Mu2SLAM: Multitask, Multilingual Speech and Language Models](https://mlsanity.com/paper/2212.09553/) (2022)
- [Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning](https://mlsanity.com/paper/2304.10309/) (2023)
- [SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations](https://mlsanity.com/paper/2212.09699/) (2022)
- [Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation](https://mlsanity.com/paper/2306.07650/) (2023)
- [An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2308.14482/) (2023)
- [Rethinking and Improving Multi-task Learning for End-to-end Speech Translation](https://mlsanity.com/paper/2311.03810/) (2023)
- [Soft Alignment of Modality Space for End-to-end Speech Translation](https://mlsanity.com/paper/2312.10952/) (2023)
- [AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation](https://mlsanity.com/paper/2212.08911/) (2022)
- [CLASP: Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval](https://mlsanity.com/paper/2412.13071/) (2024)
- [Speech Translation Refinement using Large Language Models](https://mlsanity.com/paper/2501.15090/) (2025)

## Abstract (arXiv)

The rapid development of single-modal pre-training has prompted researchers to pay more attention to cross-modal pre-training methods. In this paper, we propose a unified-modal speech-unit-text pre-training model, SpeechUT, to connect the representations of a speech encoder and a text decoder with a shared unit encoder. Leveraging hidden-unit as an interface to align speech and text, we can decompose the speech-to-text model into a speech-to-unit model and a unit-to-text model, which can be jointly pre-trained with unpaired speech and text data respectively. Our proposed SpeechUT is fine-tuned and evaluated on automatic speech recognition (ASR) and speech translation (ST) tasks. Experimental results show that SpeechUT gets substantial improvements over strong baselines, and achieves state-of-the-art performance on both the LibriSpeech ASR and MuST-C ST tasks. To better understand the proposed SpeechUT, detailed analyses are conducted. The code and pre-trained models are available at https://aka.ms/SpeechUT.