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Jun, 2024
具上下文的端到端自动语音识别及中间偏置损失
Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss
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Muhammad Shakeel, Yui Sudo, Yifan Peng, Shinji Watanabe
TL;DR
提出了一种在编码器中使用显式偏置损失作为辅助任务的方法,以更好地将文本令牌或音频帧与预期目标对齐,并通过使用 RNN-transducer 驱动的联合解码来进一步降低无偏差的单词错误率(U-WER),从而实现更强大的网络。
Abstract
contextualized
end-to-end
automatic speech recognition
has been an active research area, with recent efforts focusing on the implicit learning of contextual phrases based on the final loss objective. However, the
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