# Leveraging Pseudo-labeled Data to Improve Direct Speech-to-Speech Translation

arXiv: https://arxiv.org/abs/2205.08993 · HTML: https://mlsanity.com/paper/2205.08993/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Tacotron: Towards End-to-End Speech Synthesis](https://mlsanity.com/paper/1703.10135/) (2017)
- [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)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Direct speech-to-speech translation with discrete units](https://mlsanity.com/paper/2107.05604/) (2021)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [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)
- [ESPnet-ST: All-in-One Speech Translation Toolkit](https://mlsanity.com/paper/2004.10234/) (2020)
- [End-to-End Speech Translation with Knowledge Distillation](https://mlsanity.com/paper/1904.08075/) (2019)
- [Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/1811.02050/) (2018)
- [UWSpeech: Speech to Speech Translation for Unwritten Languages](https://mlsanity.com/paper/2006.07926/) (2020)
- [SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech Translation](https://mlsanity.com/paper/2002.12231/) (2020)

## Built on by (most cited first, 12 of 12)

- [UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units](https://mlsanity.com/paper/2212.08055/) (2022)
- [Speech-to-Speech Translation For A Real-world Unwritten Language](https://mlsanity.com/paper/2211.06474/) (2022)
- [Textless Direct Speech-to-Speech Translation with Discrete Speech Representation](https://mlsanity.com/paper/2211.00115/) (2022)
- [PolyVoice: Language Models for Speech to Speech Translation](https://mlsanity.com/paper/2306.02982/) (2023)
- [MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition](https://mlsanity.com/paper/2303.05309/) (2023)
- [Translatotron 3: Speech to Speech Translation with Monolingual Data](https://mlsanity.com/paper/2305.17547/) (2023)
- [Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation](https://mlsanity.com/paper/2210.17027/) (2022)
- [DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translation](https://mlsanity.com/paper/2310.07403/) (2023)
- [Towards cross-language prosody transfer for dialog](https://mlsanity.com/paper/2307.04123/) (2023)
- [Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data](https://mlsanity.com/paper/2212.09982/) (2022)
- [Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features](https://mlsanity.com/paper/2212.05805/) (2022)
- [Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training](https://mlsanity.com/paper/2308.16453/) (2023)

## Abstract (arXiv)

Direct Speech-to-speech translation (S2ST) has drawn more and more attention recently. The task is very challenging due to data scarcity and complex speech-to-speech mapping. In this paper, we report our recent achievements in S2ST. Firstly, we build a S2ST Transformer baseline which outperforms the original Translatotron. Secondly, we utilize the external data by pseudo-labeling and obtain a new state-of-the-art result on the Fisher English-to-Spanish test set. Indeed, we exploit the pseudo data with a combination of popular techniques which are not trivial when applied to S2ST. Moreover, we evaluate our approach on both syntactically similar (Spanish-English) and distant (English-Chinese) language pairs. Our implementation is available at https://github.com/fengpeng-yue/speech-to-speech-translation.