Does Simultaneous Speech Translation need Simultaneous Models? (2022.findings-emnlp)
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| Challenge: | Simultaneous speech translation (SimulST) systems strive for high output quality but also low latency. |
| Approach: | They propose to train SimulST offline without additional training or adaptation . they also show offline training achieves similar or better quality compared to offline training . |
| Outcome: | The proposed model can serve both offline and simultaneous applications without additional training or adaptation. |
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| Challenge: | Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster. |
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| Challenge: | Current evaluation practices in Simultaneous Speech Translation systems involve segmenting the input audio and its translations, calculating quality and latency metrics for each segment, and averaging the results. |
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| Challenge: | Simultaneous speech translation (SST) outputs translations in parallel with streaming speech input, balancing translation quality and latency. |
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| Challenge: | Existing metrics for Simultaneous speech translation (SimulST) are inaccurately measuring latency in unsegmented streaming settings. |
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| Challenge: | Existing methods to perform simultaneous speech translation always separate segmentation from the underlying model. |
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| Challenge: | Simultaneous Speech Translation (SimulST) is a task focused on ensuring high-quality translation of speech in low-latency situations. |
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| Challenge: | Existing models for simultaneous speech translation assume pre-segmented speech, limiting their real-world applicability. |
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| Challenge: | Large language models perform well in offline machine translation when the complete source sentence is provided . however, in many real scenarios, the source tokens arrive in a streaming manner and simultaneous machine translation is required . |
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| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
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