# Improving Speech-to-Speech Translation Through Unlabeled Text

arXiv: https://arxiv.org/abs/2210.14514 · HTML: https://mlsanity.com/paper/2210.14514/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Generative Spoken Language Modeling from Raw Audio](https://mlsanity.com/paper/2102.01192/) (2021)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018)
- [Direct speech-to-speech translation with discrete units](https://mlsanity.com/paper/2107.05604/) (2021)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [Textless Speech-to-Speech Translation on Real Data](https://mlsanity.com/paper/2112.08352/) (2021)
- [Direct speech-to-speech translation with a sequence-to-sequence model](https://mlsanity.com/paper/1904.06037/) (2019)
- [Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates](https://mlsanity.com/paper/1911.03167/) (2019)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/1811.02050/) (2018)
- [Cross-lingual Retrieval for Iterative Self-Supervised Training](https://mlsanity.com/paper/2006.09526/) (2020)
- [Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation](https://mlsanity.com/paper/2204.02967/) (2022)
- [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://mlsanity.com/paper/2104.06678/) (2021)
- [Towards speech-to-text translation without speech recognition](https://mlsanity.com/paper/1702.03856/) (2017)

## Built on by (most cited first, 8 of 8)

- [SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations](https://mlsanity.com/paper/2211.04508/) (2022)
- [DUB: Discrete Unit Back-translation for Speech Translation](https://mlsanity.com/paper/2305.11411/) (2023)
- [Back Translation for Speech-to-text Translation Without Transcripts](https://mlsanity.com/paper/2305.08709/) (2023)
- [DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation](https://mlsanity.com/paper/2310.07403/) (2023)
- [UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization](https://mlsanity.com/paper/2401.14664/) (2024)
- [Multilingual Speech-to-Speech Translation into Multiple Target Languages](https://mlsanity.com/paper/2307.08655/) (2023)
- [Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data?](https://mlsanity.com/paper/2406.07289/) (2024)
- [TranSentence: Speech-to-speech Translation via Language-agnostic Sentence-level Speech Encoding without Language-parallel Data](https://mlsanity.com/paper/2401.12992/) (2024)

## Abstract (arXiv)

Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been made to increase the data size from unlabeled speech by cascading pretrained speech recognition (ASR), machine translation (MT) and text-to-speech (TTS) models; unlabeled text has remained relatively under-utilized to improve S2ST. We propose an effective way to utilize the massive existing unlabeled text from different languages to create a large amount of S2ST data to improve S2ST performance by applying various acoustic effects to the generated synthetic data. Empirically our method outperforms the state of the art in Spanish-English translation by up to 2 BLEU. Significant gains by the proposed method are demonstrated in extremely low-resource settings for both Spanish-English and Russian-English translations.