Fluent Translations from Disfluent Speech in End-to-End Speech Translation (N19-1)
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| Challenge: | Disfluency removal is an intermediate step between speech recognition and machine translation (MT) with the rise of end-to-end speech translation systems, disfluency recognition and removal needs to be incorporated into the model architectures or handled as a post-processing step. |
| Approach: | They propose to use a sequence-to-sequence model to translate from noisy, disfluent speech to fluent text with disfluencies removed using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
| Outcome: | The proposed model generates fluent translations from disfluent speech using the recently collected ‘copy-edited’ references for the Fisher Spanish-English dataset. |
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| Challenge: | Disfluency detection is usually an intermediate step between an automatic speech recognition system and a downstream task. |
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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 studies have focused on disfluency detection and removal, with limited studies into its impact on downstream tasks. |
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| Challenge: | Previous work on end-to-end translation from speech uses frame-level features as speech representations, which creates longer, sparser sequences than text. |
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| Challenge: | Disfluencies in user utterances can trigger a chain of errors impacting all the modules of a dialogue system. |
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
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| Challenge: | a large number of language models struggle to handle disfluencies, authors say . when a speaker hesitates, interrupts themselves, repeats or corrects words, or abandons phrases, it can make their speech fragmented. |
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Consistent Transcription and Translation of Speech (2020.tacl-1)
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| Challenge: | Existing models that translate without transcribing focus on translation quality, while transcription receives less emphasis. |
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Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing (2025.findings-emnlp)
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| Challenge: | Existing speech translation approaches often overlook the transfer of speech patterns, leading to mismatches with source speech and limiting their suitability for dubbing applications. |
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