AdaTranS: Adapting with Boundary-based Shrinking for End-to-End Speech Translation (2023.findings-emnlp)
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| Challenge: | End-to-end speech translation (ST) models need large amount of training data to perform well. |
| Approach: | They propose a shrinking mechanism to mitigate the length mismatch between speech and text features by predicting word boundaries. |
| Outcome: | The proposed method achieves better performance on the MUST-C dataset, with higher inference speed and lower memory usage. |
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| Challenge: | End-to-end simultaneous speech translation (SST) is useful in many scenarios but has not been fully investigated. |
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
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| Challenge: | Existing approaches to improve end-to-end speech translation are limited by the availability of labeled data. |
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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. |
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
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AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation (2021.findings-acl)
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| Challenge: | End-to-end speech translation models learn acoustic representations from the encoder, which is not desirable for cross-modal and cross-lingual translation. |
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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 . |
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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. |
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Non-Parametric Domain Adaptation for End-to-End Speech Translation (2022.emnlp-main)
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| Challenge: | End-to-end speech translation (E2E-ST) systems have received increasing attention due to its less error propagation, lower latency and fewer parameters. |
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Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation (2020.acl-main)
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| Challenge: | Existing studies show that multitask learning improves speech translation performance by utilizing word embedding as the intermediate. |
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