A Paper List for Speech Translation

This is a paper list for speech translation.

Keyword: Speech Translation, Spoken Language Processing, Natural Language Processing

Paper List

Dataset

• Construction and Utilization of Bilingual Speech Corpus for Simultaneous Machine Interpretation Research, InterSpeech-2005,[paper]
• Approach to Corpus-based Interpreting Studies: Developing EPIC (European Parliament Interpreting Corpus), MuTra-2005, [paper]
• Automatic Translation from Parallel Speech: Simultaneous Interpretation as MT Training Data, ASRU-2009, [paper]
• The KIT Lecture Corpus for Speech Translation, LREC-2012, [paper]
• Improved Speech-to-Text Translation with the Fisher and Callhome Spanish–English Speech Translation Corpus, IWSLT-2013, [paper]
• Collection of a Simultaneous Translation Corpus for Comparative Analysis, LREC-2014, [paper]
• Microsoft Speech Language Translation (MSLT) Corpus: The IWSLT 2016 release for English, French and German, IWSLT-2016, [paper]
• The Microsoft Speech Language Translation (MSLT) Corpus for Chinese and Japanese: Conversational Test data for Machine Translation and Speech Recognition, Machine_Translation-2017, [paper]
• Amharic-English Speech Translation in Tourism Domain, SCNLP-2017, [paper]
• A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiment, LREC-2018, [paper]
• Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation, LREC-2018, [paper]
• A Small Griko-Italian Speech Translation Corpus, SLTU-2019, [paper]
• MuST-C: a Multilingual Speech Translation Corpus, NAACL-2019, [paper]
• MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible, Arxiv-2019, [paper]
• How2: A Large-scale Dataset for Multimodal Language Understanding, NIPS-2018, [paper]
• LibriVoxDeEn: A Corpus for German-to-English Speech Translation and Speech Recognition, LREC-2020, [paper]
• Clotho: An Audio Captioning Dataset, Arxiv-2019, [paper]
• Europarl-St: A Multilingual Corpus For Speech Translation Of Parliamentary Debates, Arxiv-2019, [paper]
• CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus, Arxiv-2020, [paper]
• MuST-Cinema: a Speech-to-Subtitles corpus, Arxiv-2020, [paper]

Pipeline ST

• Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation, ASRU-2015,[paper]
• Learning a Translation Model from Word Lattices, InterSpeech-2016, [paper]
• Learning a Lexicon and Translation Model from Phoneme Lattices, EMNLP-2016, [paper]
• Neural Lattice-to-Sequence Models for Uncertain Inputs, EMNLP-2017, [paper]
• Using Spoken Word Posterior Features in Neural Machine Translation, IWSLT-2018, [paper]
• Towards robust neural machine translation, ACL-2018, [paper]
• Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors, InterSpeech-2019, [paper]
• Lattice Transformer for Speech Translation, ACL-2019, [paper]
• Self-Attentional Models for Lattice Inputs, ACL-2019, [paper]
• Breaking the Data Barrier: Towards Robust Speech Translation via Adversarial Stability Training, IWSLT-2019, [paper]
• Neural machine translation with acoustic embedding, ASRU-2019
• Machine Translation in Pronunciation Space, Arxiv-2020, [paper]
• Diversity by Phonetics and its Application in Neural Machine Translation, AAAI-2020, [paper]
• Robust Neural Machine Translation for Clean and Noisy Speech Transcripts, IWSLT-2019, [paper]

End-to-end ST

• Towards Speech Translation of Non Written Languages, IEEE-2006, [paper]
• Towards speech-to-text translation without speech recognition, EACL-2017, [paper]
• Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation, NIPS-2016, [paper]
• An Attentional Model for Speech Translation Without Transcription, NAACL-2016, [paper]
• An Unsupervised Probability Model for Speech-to-Translation Alignment of Low-Resource Languages, EMNLP-2016, [paper]
• A Case Study on Using Speech-to-translation Alignments for Language Documentation, ComputEL-2017, [paper]
• Spoken Term Discovery for Language Documentation Using Translations, SCNLP-2017, [paper]
• Sequence-to-sequence Models Can Directly Translate Foreign Speech, InterSpeech-2017, [paper]
• Structured-based Curriculum Learning for End-to-end English-Japanese Speech Translation, InterSpeech-2017, [paper]
• End-to-End Speech Translation with the Transformer, IberSPEECH-2018, [paper]
• Towards Fluent Translations from Disfluent Speech, SLT-2018, [paper]
• Low-resource Speech-to-text Translation, InterSpeech-2018, [paper]
• End-to-End Automatic Speech Translation of Audiobooks, ICASSP-2018, [paper]
• Tied Multitask Learning for Neural Speech Translation, NAACL-2018, [paper]
• Towards Unsupervised Speech to Text Translation, ICASSP-2019, [paper]
• Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation, ICASSP-2019, [paper]
• Towards End-to-end Speech-to-text Translation with Two-pass Decoding, ICASSP-2019, [paper]
• Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation, TACL-2019, [paper]
• Direct speech-to-speech translation with a sequence-to-sequence model, InterSpeech-2019, [paper]
• Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation, TACL-2019, [paper]
• End-to-End Speech Translation with Knowledge Distillation, InterSpeech-2019, [paper]
• Fluent Translations from Disfluent Speech in End-to-End Speech Translation, NAACL-2019, [paper]
• Pre-Training On High-Resource Speech Recognition Improves Low-Resource Speech-To-Text Translation, NAACL-2019, [[paper]
• Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation, ACL-2019, [paper]
• Leveraging Out-of-Task Data for End-to-End Automatic Speech Translation, Arxiv-2019, [paper]
• Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation, AAAI-2020, [paper]
• Adapting Transformer to End-to-end Spoken Language Translation, InterSpeech-2019, [paper]
• Unsupervised phonetic and word level discovery for speech to speech translation for unwritten languages, InterSpeech-2019, [paper]
• Simuls2s: End-to-end Simultaneous Speech To Speech Translation, ICLR-2019(under review), [paper]
• Speech-To-Speech Translation Between Untranscribed Unknown Languages, ASRU-2019, [paper]
• A comparative study on end-to-end speech to text translation, ASRU-2019, [paper]
• Instance-Based Model Adaptation For Direct Speech Translation, ICASSP-2020 Submitted, [paper]
• Analyzing Asr Pretraining For Low-Resource Speech-To-Text Translation, ICASSP-2020 Submitted, [paper]
• ON-TRAC Consortium End-to-End Speech Translation Systems for the IWSLT 2019 Shared Task, IWSLT-2019, [paper]
• Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade, IWSLT-2019, [paper]
• Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning, Arxiv-2019, [paper]
• On Using SpecAugment for End-to-End Speech Translation, IWSLT-2019, [paper]
• Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding, AAAI-2020, [paper]
• From Speech-To-Speech Translation To Automatic Dubbing, Arxiv-2020, [paper]
• Skinaugment: Auto-Encoding Speaker Conversions For Automaticspeech Translation, ICASSP-2020, [paper]

Multilingual ST

• Multilingual End-To-End Speech Translation, ASRU-2019, [paper]
• One-To-Many Multilingual End-To-End Speech Translation, ASRU-2019, [paper]

Multimodal ST

• Transformer-based Cascaded Multimodal Speech Translation, Arxiv-2019, [paper]

Streaming MT

• Simultaneous translation of lectures and speeches, Machine Translation-2007, [paper]
• Real-time incremental speech-to-speech translation of dialogs, NAACL-2012, [paper]
• Incremental segmentation and decoding strategies for simultaneous translation, IJCNLP-2013, [paper]
• Don't Until the Final Verb Wait: Reinforcement learning for simultaneous machine translation, EMNLP-2014, [paper]
• Segmentation strategies for streaming speech translation, NAACL-2013, [paper]
• Optimizing segmentation strategies for simultaneous speech translation, ACL-2014, [paper]
• Syntax-based simultaneous translation through prediction of unseen syntactic constituents, ACL-IJCNLP-2015, [paper]
• Simultaneous machine translation using deep reinforcement learning, ICML-2016, [paper]
• Interpretese vs. translationese: The uniqueness of human strategies in simultaneous interpretation, NAACL-2016, [paper]
• Can neural machine translation do simultaneous translation?, Arxiv-2016, [paper]
• Learning to translate in real-time with neural machine translation, EACL-2017, [paper]
• Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation, NAACL-2018, [paper]
• Prediction Improves Simultaneous Neural Machine Translation, EMNLP-2018, [paper]
• STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework, ACL-2019, [paper]
• Simultaneous Translation with Flexible Policy via Restricted Imitation Learning, ACL-2019, [paper]
• Monotonic Infinite Lookback Attention for Simultaneous Machine Translation, ACL-2019, [paper]
• Thinking Slow about Latency Evaluation for Simultaneous Machine Translation, Arxiv-2019, [paper]
• DuTongChuan: Context-aware Translation Model for Simultaneous Interpreting, Arxiv-2019, [paper]
• Monotonic Multihead Attention, ICLR-2020(under review), [paper]
• How To Do Simultaneous Translation Better With Consecutive Neural Machine Translation, Arxiv-2019, [paper]
• Simultaneous Neural Machine Translation using Connectionist Temporal Classification, Arxiv-2019, [paper]
• Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation, Arxiv-2019, [paper]
• Learning Coupled Policies for Simultaneous Machine Translation, Arxiv-2020, [paper]

Related Works

Spoken Language Understanding

• Understanding Semantics from Speech Through Pre-training, Arxiv-2019, [paper]
• Learning ASR-Robust Contextualized Embeddings for Spoken Language Understanding, Arxiv-2019, [paper]
• A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding, Arxiv-2019, [paper]
• Incremental processing of noisy user utterances in the spoken language understanding task, W-NUT-2019, [paper]
• Adapting pretrained transformer to lattices for spoken language understanding, ASRU-2019
• Efficient semi-supervised learning for natural language understanding by optimizing diversity, ASRU-2019, [paper]
• Joint learning of word and label embeddings for sequence labelling in spoken language understanding, ASRU-2019, [paper]
• Transfer learning for context-aware spoken language understanding, ASRU-2019
• Speech Sentiment Analysis Via Pre-Trained Features From End-To-End Asr Models, Arxiv-2019, [paper]
• Recent Advances in End-to-End Spoken Language Understanding, [paper]
• Modeling Inter-Speaker Relationship In Xlnet For Contextual Spoken Language Understanding, ICASSP-2020, [paper]

Text Normalization

• A Hybrid Text Normalization System Using Multi-Head Self-Attention For Mandarin, ICASSP-2020, [paper]
• A Unified Sequence-To-Sequence Front-End Model For Mandarin Text-To-Speech Synthesis, ICASSP-2020, [paper]

Workshop

• IWSLT 2018, [link]
• IWSLT 2019, [link]
• IWSLT 2020, [link]
• AutoSimTrans 2020, [link]

Copyright

By volunteers from Institute of Automation，Chinese Academy of Sciences.
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