Papers by Qingxuan Sun

1 papers
CTC-based Non-autoregressive Speech Translation (2023.acl-long)

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Challenge: End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency.
Approach: They develop a model that uses connectionist temporal classification to predict the source and target texts.
Outcome: The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67.

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