Brian Yan, Siddharth Dalmia, Yosuke Higuchi, Graham Neubig, Florian Metze, Alan W Black, Shinji Watanabe
| Challenge: | Connectionist Temporal Classification (CTC) is widely used for automatic speech recognition (ASR) but lags behind attentional decoder approaches in terms of translation quality. |
| Approach: | They propose to use a CTC/attention framework to validate this hypothesis by modifying the Hybrid CTC-Attention model proposed for automatic speech recognition to support text-to-text translation (MT) and speech-totext translation. |
| Outcome: | The proposed model outperforms pure-attention baselines across six translation tasks. |
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| Challenge: | Using connectionist temporal classification (CTC) for speech-to-text translation is counter-intuitive due to its monotonicity assumption. |
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CTC-based Compression for Direct Speech Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). |
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| Challenge: | Existing approaches to connectionist temporal classification (CTC) are based on pre-trained language models (LMs) |
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Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation (2023.eacl-main)
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| Challenge: | Developing techniques to support end-to-end speech translation is non-trivial because of the speech-text modality gap. |
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| Challenge: | Autoregressive decoding is the only part of sequence-to-sequence models that prevents massive parallelization at inference time. |
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