由于其误差传播，延迟较少和更少的参数较少的潜力，端到端语音到文本翻译〜（e2e-st）变得越来越受欢迎。鉴于三联培训语料库$ \ langle演讲，转录，翻译\ rangle $，传统的高质量E2E-ST系统利用$ \ langle演讲，转录\ rangle $配对预先培训模型，然后利用$ \ Langle演讲，翻译\ rangle $配对进一步优化它。然而，该过程仅涉及每个阶段的两个元组数据，并且该松散耦合不能完全利用三重态数据之间的关联。在本文中，我们试图基于语音输入模拟转录和翻译的联合概率，以直接利用这种三重态数据。基于此，我们提出了一种新的正规化方法，用于改进三重态数据中双路分解协议的模型培训，理论上应该是相等的。为实现这一目标，我们将两个Kullback-Leibler发散正规化术语介绍到模型培训目的中，以减少双路径输出概率之间的不匹配。然后，训练有素的模型可以通过预定义的早期停止标签自然地被视为E2E-ST模型。 Must-C基准测试的实验表明，我们所提出的方法在所有8个语言对上显着优于最先进的E2E-ST基线，同时在自动语音识别任务中实现更好的性能。我们的代码在https://github.com/duyichao/e2e -st-tda开放。

端到端语音翻译（E2E-ST）由于其误差传播的潜力，较低的延迟和较少的参数而受到了越来越多的关注。但是，基于神经的方法对该任务的有效性受到可用培训语料库的严重限制，尤其是对于较少或不存在的域中三重障碍培训数据的领域适应性。在本文中，我们提出了一种新型的非参数方法，该方法利用特定于域的文本翻译语料库来实现E2E-ST系统的域适应性。为此，我们首先将一个附加的编码器纳入预先训练的E2E-ST模型中，以实现文本翻译建模，然后通过减少可用三重态训练数据中的通讯表示不匹配来统一解码器的输出表示形式，以实现文本和语音翻译任务。在域适应过程中，引入了K-Nearest-neighbor（KNN）分类器，以使用由域特异性文本翻译语料库构建的外部数据存储器生成最终的翻译分布，而采用通用输出表示来执行相似性搜索。 Europarl-St基准的实验表明，仅涉及内域文本翻译数据时，我们提出的方法在所有翻译方向上平均将基线显着提高了基线，即使表现出强大的强度内域微调方法。

How to solve the data scarcity problem for end-to-end speech-to-text translation (ST)? It's well known that data augmentation is an efficient method to improve performance for many tasks by enlarging the dataset. In this paper, we propose Mix at three levels for Speech Translation (M^3ST) method to increase the diversity of the augmented training corpus. Specifically, we conduct two phases of fine-tuning based on a pre-trained model using external machine translation (MT) data. In the first stage of fine-tuning, we mix the training corpus at three levels, including word level, sentence level and frame level, and fine-tune the entire model with mixed data. At the second stage of fine-tuning, we take both original speech sequences and original text sequences in parallel into the model to fine-tune the network, and use Jensen-Shannon divergence to regularize their outputs. Experiments on MuST-C speech translation benchmark and analysis show that M^3ST outperforms current strong baselines and achieves state-of-the-art results on eight directions with an average BLEU of 29.9.

End-to-end Speech Translation (E2E ST) aims to translate source speech into target translation without generating the intermediate transcript. However, existing approaches for E2E ST degrade considerably when only limited ST data are available. We observe that an ST model's performance strongly correlates with its embedding similarity from speech and transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a novel method for few-shot speech-to-text translation. Our key idea is bridging word-level representations for both modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark. Our experiments demonstrate that WACO outperforms the best baseline methods by 0.7-8.5 BLEU points with only 1-hour parallel data. Code is available at https://anonymous.4open.science/r/WACO .

We present a method for introducing a text encoder into pre-trained end-to-end speech translation systems. It enhances the ability of adapting one modality (i.e., source-language speech) to another (i.e., source-language text). Thus, the speech translation model can learn from both unlabeled and labeled data, especially when the source-language text data is abundant. Beyond this, we present a denoising method to build a robust text encoder that can deal with both normal and noisy text data. Our system sets new state-of-the-arts on the MuST-C En-De, En-Fr, and LibriSpeech En-Fr tasks.

端到端（E2E）语音到文本翻译（ST）通常取决于通过语音识别或文本翻译任务使用源成绩单预处理其编码器和/或解码器，否则翻译性能会大大下降。但是，笔录并不总是可用的，在文献中很少研究这种预处理的E2E ST。在本文中，我们重新审视了这个问题，并探讨了仅在语音翻译对培训的E2E ST质量的程度。我们重新审查了几种证明对ST的有益的技术，并提供了一系列最佳实践，这些实践使基于变压器的E2E ST系统偏向于从头开始训练。此外，我们提出了参数化的距离惩罚，以促进语音自我注意模型中的位置建模。在涵盖23种语言的四个基准测试中，我们的实验表明，在不使用任何成绩单或预处理的情况下，提议的系统达到甚至优于先前采用预处理的研究，尽管差距仍然存在（极为）低资源的设置。最后，我们讨论了神经声学特征建模，其中神经模型旨在直接从原始语音信号中提取声学特征，以简化电感偏见并为模型描述语音增添自由度。我们第一次证明了它的可行性，并在ST任务上表现出令人鼓舞的结果。

本文介绍了流媒体和非流定向晶体翻译的统一端到端帧工作。虽然非流媒体语音翻译的培训配方已经成熟，但尚未建立流媒体传播的食谱。在这项工作中，WEFOCUS在开发一个统一的模型（UNIST），它从基本组成部分的角度支持流媒体和非流媒体ST，包括培训目标，注意机制和解码政策。对最流行的语音到文本翻译基准数据集，MERE-C的实验表明，与媒体ST的BLEU评分和延迟度量有更好的折衷和液化标准端到端基线和级联模型。我们将公开提供我们的代码和评估工具。

Direct speech-to-speech translation (S2ST), in which all components can be optimized jointly, is advantageous over cascaded approaches to achieve fast inference with a simplified pipeline. We present a novel two-pass direct S2ST architecture, {\textit UnitY}, which first generates textual representations and predicts discrete acoustic units subsequently. We enhance the model performance by subword prediction in the first-pass decoder, advanced two-pass decoder architecture design and search strategy, and better training regularization. To leverage large amounts of unlabeled text data, we pre-train the first-pass text decoder based on the self-supervised denoising auto-encoding task. Experimental evaluations on benchmark datasets at various data scales demonstrate that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. We show that the proposed methods boost the performance even when predicting spectrogram in the second pass. However, predicting discrete units achieves 2.51x decoding speed-up compared to that case.

Data scarcity is one of the main issues with the end-to-end approach for Speech Translation, as compared to the cascaded one. Although most data resources for Speech Translation are originally document-level, they offer a sentence-level view, which can be directly used during training. But this sentence-level view is single and static, potentially limiting the utility of the data. Our proposed data augmentation method SegAugment challenges this idea and aims to increase data availability by providing multiple alternative sentence-level views of a dataset. Our method heavily relies on an Audio Segmentation system to re-segment the speech of each document, after which we obtain the target text with alignment methods. The Audio Segmentation system can be parameterized with different length constraints, thus giving us access to multiple and diverse sentence-level views for each document. Experiments in MuST-C show consistent gains across 8 language pairs, with an average increase of 2.2 BLEU points, and up to 4.7 BLEU for lower-resource scenarios in mTEDx. Additionally, we find that SegAugment is also applicable to purely sentence-level data, as in CoVoST, and that it enables Speech Translation models to completely close the gap between the gold and automatic segmentation at inference time.

本文介绍了我们针对IWSLT 2022离线任务的端到端Yitrans语音翻译系统的提交，该任务从英语音频转换为德语，中文和日语。 Yitrans系统建立在大规模训练的编码器模型上。更具体地说，我们首先设计了多阶段的预训练策略，以建立具有大量标记和未标记数据的多模式模型。然后，我们为下游语音翻译任务微调模型的相应组件。此外，我们做出了各种努力，以提高性能，例如数据过滤，数据增强，语音细分，模型集合等。实验结果表明，我们的Yitrans系统比在三个翻译方向上的强基线取得了显着改进，并且比去年在TST2021英语 - 德国人中的最佳端到端系统方面的改进+5.2 BLEU改进。根据自动评估指标，我们的最终意见在英语 - 德国和英语端到端系统上排名第一。我们使代码和模型公开可用。

我们介绍了Fairseq S2T，这是语音到文本（S2T）建模任务的Fairseq扩展，例如端到端语音识别和语音到文本翻译。它遵循Fairseq的仔细设计，以实现可扩展性和可扩展性。我们提供从数据预处理，模型培训到离线推理的端到端工作流程。我们实施了基于最新的RNN，基于变压器以及基于构象的模型和开源详细培训配方。Fairseq的机器翻译模型和语言模型可以无缝集成到S2T工作流中，以进行多任务学习或转移学习。Fairseq S2T文档和示例可在https://github.com/pytorch/fairseq/tree/master/master/examples/speech_to_text上获得。

To alleviate the data scarcity problem in End-to-end speech translation (ST), pre-training on data for speech recognition and machine translation is considered as an important technique. However, the modality gap between speech and text prevents the ST model from efficiently inheriting knowledge from the pre-trained models. In this work, we propose AdaTranS for end-to-end ST. It adapts the speech features with a new shrinking mechanism to mitigate the length mismatch between speech and text features by predicting word boundaries. Experiments on the MUST-C dataset demonstrate that AdaTranS achieves better performance than the other shrinking-based methods, with higher inference speed and lower memory usage. Further experiments also show that AdaTranS can be equipped with additional alignment losses to further improve performance.

本报告介绍了在大型多语种计算机翻译中为WMT21共享任务的Microsoft的机器翻译系统。我们参加了所有三种评估轨道，包括大轨道和两个小轨道，前者是无约束的，后两者完全受约束。我们的模型提交到共享任务的初始化用deltalm \脚注{\ url {https://aka.ms/deltalm}}，一个通用的预训练的多语言编码器 - 解码器模型，并相应地使用巨大的收集并行进行微调数据和允许的数据源根据轨道设置，以及应用逐步学习和迭代背翻译方法进一步提高性能。我们的最终提交在自动评估度量方面排名第一的三条轨道。

Speech translation (ST) is the task of directly translating acoustic speech signals in a source language into text in a foreign language. ST task has been addressed, for a long time, using a pipeline approach with two modules : first an Automatic Speech Recognition (ASR) in the source language followed by a text-to-text Machine translation (MT). In the past few years, we have seen a paradigm shift towards the end-to-end approaches using sequence-to-sequence deep neural network models. This paper presents our efforts towards the development of the first Broadcast News end-to-end Arabic to English speech translation system. Starting from independent ASR and MT LDC releases, we were able to identify about 92 hours of Arabic audio recordings for which the manual transcription was also translated into English at the segment level. These data was used to train and compare pipeline and end-to-end speech translation systems under multiple scenarios including transfer learning and data augmentation techniques.

We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available.

自动副标题是将视听产品的语音自动转化为短文本的任务，换句话说，字幕及其相应的时间戳。生成的字幕需要符合多个空间和时间要求（长度，阅读速度），同时与语音同步并以促进理解的方式进行分割。鉴于其相当大的复杂性，迄今为止，通过分别处理转录，翻译，分割为字幕并预测时间戳的元素来解决自动字幕。在本文中，我们提出了第一个直接自动字幕模型，该模型在单个解决方案中从源语音中生成目标语言字幕及其时间戳。与经过内外数据和外域数据训练的最先进的级联模型的比较表明，我们的系统提供了高质量的字幕，同时在整合性方面也具有竞争力，并具有维护单个模型的所有优势。

我们介绍了一种无线文字语音转换（S2ST）系统，可以将来自一种语言的语音转换为另一种语言，并且可以在不需要任何文本数据的情况下构建。与文献中的现有工作不同，我们解决了模拟多扬声器目标语音的挑战，并用现实世界的S2ST数据训练系统。我们方法的关键是一种自我监督的单位语音标准化技术，该标准化技术将预先训练的语音编码器具有来自多个扬声器的配对声音，以及单个参考扬声器，以减少由于复印件引起的变化，同时保留词汇内容。只有10分钟的语音标准化的配对数据，我们在培训\ vp〜s2st数据集上的S2ST模型时获得平均3.2 BLEU增益，而不是在未标准化的语音目标上培训的基线。我们还将自动开采的S2ST数据纳入并显示额外的2.0 BLEU增益。据我们所知，我们是第一个建立无线的S2ST技术，可以用真实世界的数据培训，并为多种语言配对工作。

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective . mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.

We present Mu$^{2}$SLAM, a multilingual sequence-to-sequence model pre-trained jointly on unlabeled speech, unlabeled text and supervised data spanning Automatic Speech Recognition (ASR), Automatic Speech Translation (AST) and Machine Translation (MT), in over 100 languages. By leveraging a quantized representation of speech as a target, Mu$^{2}$SLAM trains the speech-text models with a sequence-to-sequence masked denoising objective similar to T5 on the decoder and a masked language modeling (MLM) objective on the encoder, for both unlabeled speech and text, while utilizing the supervised tasks to improve cross-lingual and cross-modal representation alignment within the model. On CoVoST AST, Mu$^{2}$SLAM establishes a new state-of-the-art for models trained on public datasets, improving on xx-en translation over the previous best by 1.9 BLEU points and on en-xx translation by 1.1 BLEU points. On Voxpopuli ASR, our model matches the performance of an mSLAM model fine-tuned with an RNN-T decoder, despite using a relatively weaker sequence-to-sequence architecture. On text understanding tasks, our model improves by more than 6\% over mSLAM on XNLI, getting closer to the performance of mT5 models of comparable capacity on XNLI and TydiQA, paving the way towards a single model for all speech and text understanding tasks.

语音细分将长言语分为短段，对于语音翻译（ST）至关重要。像WebRTC VAD这样的流行VAD工具通常依赖于基于暂停的细分。不幸的是，语音中的暂停不一定与句子边界匹配，句子可以通过很短的停顿连接，而VAD很难检测到。在这项研究中，我们建议使用使用分割的双语语音语料库训练的二元分类模型进行语音分割方法。我们还提出了一种结合VAD和上述语音分割方法的混合方法。实验结果表明，所提出的方法比常规分割方法更适合级联和端到端ST系统。混合方法进一步改善了翻译性能。