# Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders

arXiv: https://arxiv.org/abs/2105.05752 · HTML: https://mlsanity.com/paper/2105.05752/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Sequence-to-Sequence Models Can Directly Translate Foreign Speech](https://mlsanity.com/paper/1703.08581/) (2017)
- [Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/1612.01744/) (2016)
- [ESPnet-ST: All-in-One Speech Translation Toolkit](https://mlsanity.com/paper/2004.10234/) (2020)
- [Pre-training on high-resource speech recognition improves low-resource speech-to-text translation](https://mlsanity.com/paper/1809.01431/) (2018)
- [End-to-End Automatic Speech Translation of Audiobooks](https://mlsanity.com/paper/1802.04200/) (2018)
- [End-to-End Speech Translation with Knowledge Distillation](https://mlsanity.com/paper/1904.08075/) (2019)
- [Curriculum Pre-training for End-to-End Speech Translation](https://mlsanity.com/paper/2004.10093/) (2020)
- [Bridging the Modality Gap for Speech-to-Text Translation](https://mlsanity.com/paper/2010.14920/) (2020)
- [Tied Multitask Learning for Neural Speech Translation](https://mlsanity.com/paper/1802.06655/) (2018)
- [Modeling Localness for Self-Attention Networks](https://mlsanity.com/paper/1810.10182/) (2018)
- [Towards Making the Most of BERT in Neural Machine Translation](https://mlsanity.com/paper/1908.05672/) (2019)
- [A Comparative Study on End-to-end Speech to Text Translation](https://mlsanity.com/paper/1911.08870/) (2019)
- [Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation](https://mlsanity.com/paper/1909.07575/) (2019)
- [Self-Training for End-to-End Speech Translation](https://mlsanity.com/paper/2006.02490/) (2020)
- [Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation](https://mlsanity.com/paper/1802.03142/) (2018)
- [Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation](https://mlsanity.com/paper/1904.07209/) (2019)
- [Self-Attentional Acoustic Models](https://mlsanity.com/paper/1803.09519/) (2018)
- [Consecutive Decoding for Speech-to-text Translation](https://mlsanity.com/paper/2009.09737/) (2020)
- [Analyzing ASR pretraining for low-resource speech-to-text translation](https://mlsanity.com/paper/1910.10762/) (2019)
- [Learning Light-Weight Translation Models from Deep Transformer](https://mlsanity.com/paper/2012.13866/) (2020)

## Built on by (most cited first, 30 of 48)

- [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)
- [SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training](https://mlsanity.com/paper/2210.03730/) (2022)
- [Revisiting End-to-End Speech-to-Text Translation From Scratch](https://mlsanity.com/paper/2206.04571/) (2022)
- [Pre-training for Speech Translation: CTC Meets Optimal Transport](https://mlsanity.com/paper/2301.11716/) (2023)
- [CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation](https://mlsanity.com/paper/2305.14635/) (2023)
- [M3ST: Mix at Three Levels for Speech Translation](https://mlsanity.com/paper/2212.03657/) (2022)
- [WACO: Word-Aligned Contrastive Learning for Speech Translation](https://mlsanity.com/paper/2212.09359/) (2022)
- [T-Modules: Translation Modules for Zero-Shot Cross-Modal Machine Translation](https://mlsanity.com/paper/2205.12216/) (2022)
- [M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2207.00952/) (2022)
- [Understanding and Bridging the Modality Gap for Speech Translation](https://mlsanity.com/paper/2305.08706/) (2023)
- [Recent Advances in Direct Speech-to-text Translation](https://mlsanity.com/paper/2306.11646/) (2023)
- [Efficient yet Competitive Speech Translation: FBK@IWSLT2022](https://mlsanity.com/paper/2205.02629/) (2022)
- [When Good and Reproducible Results are a Giant with Feet of Clay: The Importance of Software Quality in NLP](https://mlsanity.com/paper/2303.16166/) (2023)
- [Pushing the Limits of Zero-shot End-to-End Speech Translation](https://mlsanity.com/paper/2402.10422/) (2024)
- [Non-autoregressive End-to-end Speech Translation with Parallel Autoregressive Rescoring](https://mlsanity.com/paper/2109.04411/) (2021)
- [Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates](https://mlsanity.com/paper/2109.12804/) (2021)
- [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)
- [CTC-based Non-autoregressive Speech Translation](https://mlsanity.com/paper/2305.17358/) (2023)
- [Tuning Large language model for End-to-end Speech Translation](https://mlsanity.com/paper/2310.02050/) (2023)
- [Genre-conditioned Acoustic Models for Automatic Lyrics Transcription of Polyphonic Music](https://mlsanity.com/paper/2204.03307/) (2022)
- [Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning](https://mlsanity.com/paper/2304.10309/) (2023)
- [Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation](https://mlsanity.com/paper/2210.09556/) (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)
- [Does Joint Training Really Help Cascaded Speech Translation?](https://mlsanity.com/paper/2210.13700/) (2022)
- [Soft Alignment of Modality Space for End-to-end Speech Translation](https://mlsanity.com/paper/2312.10952/) (2023)

## Abstract (arXiv)

Encoder pre-training is promising in end-to-end Speech Translation (ST), given the fact that speech-to-translation data is scarce. But ST encoders are not simple instances of Automatic Speech Recognition (ASR) or Machine Translation (MT) encoders. For example, we find that ASR encoders lack the global context representation, which is necessary for translation, whereas MT encoders are not designed to deal with long but locally attentive acoustic sequences. In this work, we propose a Stacked Acoustic-and-Textual Encoding (SATE) method for speech translation. Our encoder begins with processing the acoustic sequence as usual, but later behaves more like an MT encoder for a global representation of the input sequence. In this way, it is straightforward to incorporate the pre-trained models into the system. Also, we develop an adaptor module to alleviate the representation inconsistency between the pre-trained ASR encoder and MT encoder, and develop a multi-teacher knowledge distillation method to preserve the pre-training knowledge. Experimental results on the LibriSpeech En-Fr and MuST-C En-De ST tasks show that our method achieves state-of-the-art BLEU scores of 18.3 and 25.2. To our knowledge, we are the first to develop an end-to-end ST system that achieves comparable or even better BLEU performance than the cascaded ST counterpart when large-scale ASR and MT data is available.