Generating Synthetic Speech from SpokenVocab for Speech Translation (2023.findings-eacl)
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| Challenge: | End-to-end speech-totext translation (ST) models require large amounts of data to train, but their size is considerably smaller than text-based MT data. |
| Approach: | They propose a method to convert MT data to ST data via text-to-speech systems. |
| Outcome: | The proposed method improves translation quality by an average of 1.83 BLEU score while performing equally well as TTS-generated speech in improving translation quality. |
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Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
| Outcome: | The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages. |
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)
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| Challenge: | End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations. |
| Approach: | They propose a method that augments transcriptions by sampling from suffix memory and translating them into target languages. |
| Outcome: | The proposed method delivers up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on languages on CoVoST 2 and Europarl-ST. |
From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
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| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
| Outcome: | The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%. |
Can We Achieve High-quality Direct Speech-to-Speech Translation without Parallel Speech Data? (2024.acl-long)
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| Challenge: | Existing two-pass direct speech-to-speech translation models require parallel speech data to train, which is challenging to collect. |
| Approach: | They propose a two-pass direct speech-to-speech translation (S2ST) model that decomposes the task into speech- to-text translation (s2TT) and text-tospech (TTS) they propose 'composer' S2ST model that integrates pretrained S2TT and TTS models into a direct S2 ST model. |
| Outcome: | The proposed model integrates pretrained S2TT and TTS models into a direct S2ST model without parallel speech data. |
Back Translation for Speech-to-text Translation Without Transcripts (2023.acl-long)
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| Challenge: | End-to-end speech-totext translation (ST) is often achieved by utilizing source transcripts, but transcripts are only sometimes available since numerous unwritten languages exist worldwide. |
| Approach: | They propose an algorithm to synthesize pseudo ST data from monolingual target data to enhance ST without generating source transcripts. |
| Outcome: | The proposed method achieves an average boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. |
Pushing the Limits of Zero-shot End-to-End Speech Translation (2024.findings-acl)
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| Challenge: | Existing approaches to end-to-end Speech Translation (ST) systems require limited data, which can cause data scarcity and performance degradation. |
| Approach: | They propose a method for zero-shot ST that bridges the modality gap without any paired ST data. |
| Outcome: | The proposed method bridges the modality gap without any paired ST data on a speech encoder and on MT models. |
SegAugment: Maximizing the Utility of Speech Translation Data with Segmentation-based Augmentations (2023.findings-emnlp)
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| Challenge: | End-to-end Speech Translation models are limited by a data bottleneck . end-to end models can address several shortcomings of cascaded models . |
| Approach: | They propose a data augmentation strategy to augment sentence-level datasets by using an Audio Segmentation system to re-segment the speech of each document with different length constraints. |
| Outcome: | The proposed method achieves state-of-the-art results in MuST-C and in mTEDx. |
Streaming Models for Joint Speech Recognition and Translation (2021.eacl-main)
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| Challenge: | Using end-to-end models for speech translation has become a focus of the ST community . cascaded models have the advantage of including automatic speech recognition output . |
| Approach: | They propose a model that condenses sound waves into translated text and integrates automatic speech recognition outputs into the models. |
| Outcome: | The proposed model is statistically similar to cascading models, but has half the number of parameters. |
Phone Features Improve Speech Translation (2020.acl-main)
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| Challenge: | End-to-end models for speech translation more tightly couple speech recognition (ASR) and machine translation (MT) compared to cascades, but performance gap remains in low-resource conditions . |
| Approach: | They propose two methods to incorporate phone features into current neural speech translation models. |
| Outcome: | The proposed models outperform existing models and cascades by up to 9 BLEU on low-resource conditions. |
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025.coling-main)
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| Challenge: | More than half of the world's population is presumed to be bilingual . spoken translation of code-switched speech has been under-explored . |
| Approach: | They propose an end-to-end model architecture CoSTA that scaffolds on pretrained ASR and MT modules. |
| Outcome: | The proposed model outperforms existing models by 3.5 BLEU points in spoken translation of code-switched speech. |