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English(EN) Reconstruct! Don't Encode: Self-Supervised Representation Reconstruction Loss for High-Intelligibility and Low-Latency Streaming Neural Audio Codec

新的SSRR损失提升神经音频编解码器的可懂度和速度

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的研究论文介绍了一种用于神经音频编解码器的自监督表征重构（SSRR）损失，旨在提高可懂度和降低延迟。该方法加速了训练，使得在H200 GPU等硬件上只需更少的步骤即可取得有竞争力的结果。SSRR损失通过重构从编解码器输出中提取的自监督表征来提高语音可懂度，从而在基于Transformer的编解码器中实现零前瞻的实时部署。采用此方法的JHCodec在LibriSpeech test-clean数据集上实现了优于其他方法的词错误率和字错误率，同时保持了低延迟。 AI

影响 这种新的SSRR损失方法可能带来更具可懂度和更低延迟的音频编解码器，从而惠及实时通信和语音处理应用。

排序理由 详细介绍神经音频编解码器新方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SSRR损失提升神经音频编解码器的可懂度和速度

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详细介绍神经音频编解码器新方法的 ist 研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Junhyeok Lee, Xiluo He, Jihwan Lee, Helin Wang, Shrikanth Narayanan, Thomas Thebaud, Laureano Moro-Velazquez, Jes\'us Villalba, Najim Dehak ·

    重构！而非编码：用于高可懂度和低延迟流式神经音频编解码器的自监督表示重构损失

    arXiv:2603.05887v2 Announce Type: replace-cross Abstract: Neural audio codecs optimized for mel-spectrogram reconstruction often fail to preserve intelligibility. While semantic encoder distillation improves encoded representations, it does not guarantee content preservation in r…

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