End-to-End Speech Translation Progress

Tutorial

• EACL 2021 tutorial: Speech Translation
• Blog: Getting Started with End-to-End Speech Translation
• ACL 2020 Theme paper: Speech Translation and the End-to-End Promise: Taking Stock of Where We Are
• INTERSPEECH 2019 survey talk: Spoken Language Translation

Data

Corpus	Direction	Target	Duration	License
CoVoST 2	{Fr, De, Es, Ca, It, Ru, Zh, Pt, Fa, Et, Mn, Nl, Tr, Ar, Sv, Lv, Sl, Ta, Ja, Id, Cy} -> En and En -> {De, Ca, Zh, Fa, Et, Mn, Tr, Ar, Sv, Lv, Sl, Ta, Ja, Id, Cy}	Text	2880h	CC0
CVSS	{Fr, De, Es, Ca, It, Ru, Zh, Pt, Fa, Et, Mn, Nl, Tr, Ar, Sv, Lv, Sl, Ta, Ja, Id, Cy} -> En	Text & Speech	1900h	CC BY 4.0
mTEDx	{Es, Fr, Pt, It, Ru, El} -> En, {Fr, Pt, It} -> Es, Es -> {Fr, It}, {Es,Fr} -> Pt	Text	765h	CC BY-NC-ND 4.0
CoVoST	{Fr, De, Nl, Ru, Es, It, Tr, Fa, Sv, Mn, Zh} -> En	Text	700h	CC0
MUST-C & MUST-Cinema	En -> {De, Es, Fr, It, Nl, Pt, Ro, Ru, Ar, Cs, Fa, Tr, Vi, Zh}	Text	504h	CC BY-NC-ND 4.0
How2	En -> Pt	Text	300h	Youtube & CC BY-SA 4.0
Augmented LibriSpeech	En -> Fr	Text	236h	CC BY 4.0
Europarl-ST	{En, Fr, De, Es, It, Pt, Pl, Ro, Nl} -> {En, Fr, De, Es, It, Pt, Pl, Ro, Nl}	Text	280h	CC BY-NC 4.0
Kosp2e	Ko -> En	Text	198h	Mixed CC
Fisher + Callhome	Es -> En	Text	160h+20h	LDC
MaSS	parallel among En, Es, Eu, Fi, Fr, Hu, Ro and Ru	Text & Speech	172h	Bible.is
LibriVoxDeEn	De -> En	Text	110h	CC BY-NC-SA 4.0
Prabhupadavani	parallel among En, Fr, De, Gu, Hi, Hu, Id, It, Lv, Lt, Ne, Fa, Pl, Pt, Ru, Sl, Sk, Es, Se, Ta, Te, Tr, Bg, Hr, Da and Nl	Text	94h
BSTC	Zh -> En	Text	68h
LibriS2S	De <-> En	Text & Speech	52h/57h	CC BY-NC-SA 4.0

Toolkit

• ESPNet-ST
• Fairseq S2T
• NeurST

Paper

2023

• [arXiv] Tuning Large language model for End-to-end Speech Translation
• [arXiv] Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning
• [arXiv] Multilingual Speech-to-Speech Translation into Multiple Target Languages
• [ICCV] MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition
• [INTERSPEECH] MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation
• [INTERSPEECH] Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer
• [INTERSPEECH] Joint Speech Translation and Named Entity Recognition
• [INTERSPEECH] StyleS2ST: Zero-shot Style Transfer for Direct Speech-to-speech Translation
• [INTERSPEECH] Knowledge Distillation on Joint Task End-to-End Speech Translation
• [INTERSPEECH] GigaST: A 10,000-hour Pseudo Speech Translation Corpus
• [INTERSPEECH] Inter-connection: Effective Connection between Pre-trained Encoder and Decoder for Speech Translation
• [INTERSPEECH] AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translation
• [INTERSPEECH] Incremental Blockwise Beam Search for Simultaneous Speech Translation with Controllable Quality-Latency Tradeoff
• [INTERSPEECH] HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
• [INTERSPEECH] Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models
• [ICML] Pre-training for Speech Translation: CTC Meets Optimal Transport
• [ACL] UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units
• [ACL] Simple and effective unsupervised speech translation
• [ACL] BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric
• [ACL] SpeechMatrix: A Large-Scale Mined Corpus of Multilingual Speech-to-Speech Translations
• [ACL] Understanding and Bridging the Modality Gap for Speech Translation
• [ACL] Back Translation for Speech-to-text Translation Without Transcripts
• [ACL] AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation
• [ACL] WACO: Word-Aligned Contrastive Learning for Speech Translation
• [ACL] Attention as a guide for Simultaneous Speech Translation
• [ACL Findings] Speech-to-Speech Translation for a Real-world Unwritten Language
• [ACL Findings] CKDST: Comprehensively and Effectively Distill Knowledge from Machine Translation to End-to-End Speech Translation
• [ACL Findings] Duplex Diffusion Models Improve Speech-to-Speech Translation
• [ACL Findings] DUB: Discrete Unit Back-translation for Speech Translation
• [ACL Findings] End-to-End Simultaneous Speech Translation with Differentiable Segmentation
• [ACL Findings] Implicit Memory Transformer for Computationally Efficient Simultaneous Speech Translation
• [ACL Findings] Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
• [ICASSP] Textless Direct Speech-to-Speech Translation with Discrete Speech Representation
• [ICASSP] M3ST: Mix at Three Levels for Speech Translation
• [EACL Findings] Generating Synthetic Speech from SpokenVocab for Speech Translation
• [AAAI] Improving End-to-end Speech Translation by Leveraging Auxiliary Speech and Text Data

2022

• [arXiv] AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation
• [arXiv] Direct Speech-to-speech Translation without Textual Annotation using Bottleneck Features
• [arXiv] ArzEn-ST: A Three-way Speech Translation Corpus for Code-Switched Egyptian Arabic - English
• [arXiv] Multilingual Simultaneous Speech Translation
• [arXiv] Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
• [EMNLP Findings] Does Simultaneous Speech Translation need Simultaneous Models?
• [EMNLP Findings] RedApt: An Adaptor for WAV2VEC 2 Encoding Faster and Smaller Speech Translation without Quality Compromise
• [INTERSPEECH] Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation
• [INTERSPEECH] Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
• [INTERSPEECH] Large-Scale Streaming End-to-End Speech Translation with Neural Transducers
• [INTERSPEECH] Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation
• [INTERSPEECH] Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation
• [INTERSPEECH] SHAS: Approaching optimal Segmentation for End-to-End Speech Translation
• [INTERSPEECH] Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech Translation
• [INTERSPEECH] M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation
• [NAACL] Textless Speech-to-Speech Translation on Real Data
• [ICML] Revisiting End-to-End Speech-to-Text Translation From Scratch
• [ICML] Translatotron 2: Robust direct speech-to-speech translation
• [ACL] Learning When to Translate for Streaming Speech
• [ACL] Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation
• [ACL] UniST: Unified End-to-end Model for Streaming and Non-streaming Speech Translation
• [ACL] Direct speech-to-speech translation with discrete units
• [ACL] STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
• [ACL Findings] End-to-End Speech Translation for Code Switched Speech
• [LREC] CVSS Corpus and Massively Multilingual Speech-to-Speech Translation
• [LREC] LibriS2S: A German-English Speech-to-Speech Translation Corpus
• [ICASSP] Tackling data scarcity in speech translation using zero-shot multilingual machine translation techniques
• [NN] Improving data augmentation for low resource speech-to-text translation with diverse paraphrasing
• [AAAI] Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement

2021

• [arXiv] Efficient Transformer for Direct Speech Translation
• [arXiv] Zero-shot Speech Translation
• [arXiv] Direct Simultaneous Speech-to-Speech Translation with Variational Monotonic Multihead Attention
• [ASRU] Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates
• [ASRU] Assessing Evaluation Metrics for Speech-to-Speech Translation
• [ASRU] Enabling Zero-shot Multilingual Spoken Language Translation with Language-Specific Encoders and Decoders
• [ICNLSP] Beyond Voice Activity Detection: Hybrid Audio Segmentation for Direct Speech Translation
• [INTERSPEECH] Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translation
• [EMNLP] Speechformer: Reducing Information Loss in Direct Speech Translation
• [EMNLP] Is "moby dick" a Whale or a Bird? Named Entities and Terminology in Speech Translation
• [EMNLP] Mutual-Learning Improves End-to-End Speech Translation
• [INTERSPEECH] End-to-end Speech Translation via Cross-modal Progressive Training
• [INTERSPEECH] CoVoST 2 and Massively Multilingual Speech-to-Text Translation
• [INTERSPEECH] The Multilingual TEDx Corpus for Speech Recognition and Translation
• [INTERSPEECH] Large-Scale Self-and Semi-Supervised Learning for Speech Translation
• [INTERSPEECH] Kosp2e: Korean Speech to English Translation Corpus
• [INTERSPEECH] AlloST: Low-resource Speech Translation without Source Transcription
• [INTERSPEECH] SpecRec: An Alternative Solution for Improving End-to-End Speech-to-Text Translation via Spectrogram Reconstruction
• [INTERSPEECH] Optimally Encoding Inductive Biases into the Transformer Improves End-to-End Speech Translation
• [INTERSPEECH] ASR Posterior-based Loss for Multi-task End-to-end Speech Translation
• [AMTA] Simultaneous Speech Translation for Live Subtitling: from Delay to Display
• [ACL] Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders
• [ACL] Multilingual Speech Translation with Efficient Finetuning of Pretrained Models
• [ACL] Lightweight Adapter Tuning for Multilingual Speech Translation
• [ACL] Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?
• [ACL] Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task
• [ACL] Beyond Sentence-Level End-to-End Speech Translation: Context Helps
• [ACL Findings] Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR
• [ACL Findings] AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation
• [ACL Findings] RealTranS: End-to-End Simultaneous Speech Translation with Convolutional Weighted-Shrinking Transformer
• [ACL Findings] Learning Shared Semantic Space for Speech-to-Text Translation
• [ACL Findings] Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation
• [ACL Findings] How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation
• [ACL Demo] NeurST: Neural Speech Translation Toolkit
• [ICML] Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation
• [NAACL] Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation
• [NAACL] Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
• [NAACL AutoSimTrans] BSTC: A Large-Scale Chinese-English Speech Translation Dataset
• [AmericasNLP] Highland Puebla Nahuatl–Spanish Speech Translation Corpus for Endangered Language Documentation
• [ICASSP] Task Aware Multi-Task Learning for Speech to Text Tasks
• [ICASSP] A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks
• [ICASSP] An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies
• [ICASSP] Streaming Simultaneous Speech Translation with Augmented Memory Transformer
• [ICASSP] Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
• [ICASSP] Cascaded Models With Cyclic Feedback For Direct Speech Translation
• [ICASSP] Jointly Trained Transformers models for Spoken Language Translation
• [ICASSP] Efficient Use of End-to-end Data in Spoken Language Processing
• [EACL] CTC-based Compression for Direct Speech Translation
• [EACL] Streaming Models for Joint Speech Recognition and Translation
• [IberSPEECH] mintzai-ST: Corpus and Baselines for Basque-Spanish Speech Translation
• [AAAI] Consecutive Decoding for Speech-to-text Translation
• [AAAI] UWSpeech: Speech to Speech Translation for Unwritten Languages
• [AAAI] "Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
• [SLT] Tight Integrated End-to-End Training for Cascaded Speech Translation
• [SLT] Transformer-based Direct Speech-to-speech Translation with Transcoder

2020

• [arXiv] Bridging the Modality Gap for Speech-to-Text Translation
• [arXiv] CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning
• [CLiC-IT] On Knowledge Distillation for Direct Speech Translation
• [COLING] Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation
• [COLING] Breeding Gender-aware Direct Speech Translation Systems
• [AACL] SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation
• [AACL Demo] fairseq S2T: Fast Speech-to-Text Modeling with fairseq
• [EMNLP] Effectively pretraining a speech translation decoder with Machine Translation data
• [EMNLP Findings] Adaptive Feature Selection for End-to-End Speech Translation
• [AMTA] On Target Segmentation for Direct Speech Translation
• [INTERSPEECH] Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
• [INTERSPEECH] Relative Positional Encoding for Speech Recognition and Direct Translation
• [INTERSPEECH] Contextualized Translation of Automatically Segmented Speech
• [INTERSPEECH] Self-Training for End-to-End Speech Translation
• [INTERSPEECH] Improving Cross-Lingual Transfer Learning for End-to-End Speech Recognition with Speech Translation
• [INTERSPEECH] Self-Supervised Representations Improve End-to-End Speech Translation
• [INTERSPEECH] Investigating Self-Supervised Pre-Training for End-to-End Speech Translation
• [TACL] Consistent Transcription and Translation of Speech
• [ACL] Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
• [ACL] Phone Features Improve Speech Translation
• [ACL] Curriculum Pre-training for End-to-End Speech Translation
• [ACL] SimulSpeech: End-to-End Simultaneous Speech to Text Translation
• [ACL] Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus
• [ACL Theme] Speech Translation and the End-to-End Promise: Taking Stock of Where We Are
• [ACL Demo] ESPnet-ST: All-in-One Speech Translation Toolkit
• [LREC] CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus
• [LREC] MuST-Cinema: a Speech-to-Subtitles corpus
• [LREC] MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible
• [LREC] LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition
• [ICASSP] Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates
• [ICASSP] Instance-Based Model Adaptation For Direct Speech Translation
• [ICASSP] Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning
• [ICASSP] Analyzing ASR pretraining for low-resource speech-to-text translation
• [ICASSP] End-to-End Speech Translation with Self-Contained Vocabulary Manipulation
• [AAAI] Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation
• [AAAI] Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding

2019

• [ASRU] One-To-Many Multilingual End-to-end Speech Translation
• [ASRU] Multilingual End-to-End Speech Translation
• [ASRU] Speech-to-speech Translation between Untranscribed Unknown Languages
• [ASRU] A Comparative Study on End-to-end Speech to Text Translation
• [IWSLT] Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade
• [IWSLT] On Using SpecAugment for End-to-End Speech Translation
• [INTERSPEECH] End-to-End Speech Translation with Knowledge Distillation
• [INTERSPEECH] Adapting Transformer to End-to-end Spoken Language Translation
• [INTERSPEECH] Direct speech-to-speech translation with a sequence-to-sequence model
• [ACL] Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation
• [ACL] Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
• [NAACL] Pre-training on High-Resource Speech Recognition Improves Low-Resource Speech-to-Text Translation
• [NAACL] MuST-C: a Multilingual Speech Translation Corpus
• [NAACL] Fluent Translations from Disfluent Speech in End-to-End Speech Translation
• [ICASSP] Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation
• [ICASSP] Towards unsupervised speech-to-text translation
• [ICASSP] Towards End-to-end Speech-to-text Translation with Two-pass Decoding

2018

• [NIPS] How2: A Large-scale Dataset for Multimodal Language Understanding
• [IberSPEECH] End-to-End Speech Translation with the Transformer
• [INTERSPEECH] Low-Resource Speech-to-Text Translation
• [LREC] Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation
• [NAACL] Tied multitask learning for neural speech translation
• [ICASSP] End-to-End Automatic Speech Translation of Audiobooks

2017

• [INTERSPEECH] Sequence-to-Sequence Models Can Directly Translate Foreign Speech
• [INTERSPEECH] Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation
• [EACL] Towards speech-to-text translation without speech recognition

2016

• [NIPS Workshop] Listen and translate: A proof of concept for end-to-end speech-to-text translation
• [NAACL] An Attentional Model for Speech Translation Without Transcription

2013

• [IWSLT] Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus

Contact

Changhan Wang ([email protected])