论文标题
探索扬声器诊断基于检测的方法 @ ego4d唯一的诊断挑战2022
Exploring Detection-based Method For Speaker Diarization @ Ego4D Audio-only Diarization Challenge 2022
论文作者
论文摘要
我们在ECCV 2022中提供了仅EGO4D音频诊断挑战的技术报告。扬声器诊断将音频流视为输入，并根据说话者的身份输出均匀的片段。它旨在解决“谁讲话何时说话”的问题。在本文中，我们探讨了一种基于检测的方法，以应对仅声音扬声器诊断任务。我们的方法首先通过音频主干提取音频功能，然后将功能馈送到检测生成网络以获取扬声器建议。最后，经过后处理后，我们可以得到诊断结果。验证数据集验证了此方法，我们的方法在测试数据集上实现了53.85 der。这些结果在2022年EGO4D Audio diarization Challenge的排行榜上排名第三。
We provide the technical report for Ego4D audio-only diarization challenge in ECCV 2022. Speaker diarization takes the audio streams as input and outputs the homogeneous segments according to the speaker's identity. It aims to solve the problem of "Who spoke when." In this paper, we explore a Detection-based method to tackle the audio-only speaker diarization task. Our method first extracts audio features by audio backbone and then feeds the feature to a detection-generate network to get the speaker proposals. Finally, after postprocessing, we can get the diarization results. The validation dataset validates this method, and our method achieves 53.85 DER on the test dataset. These results rank 3rd on the leaderboard of Ego4D audio-only diarization challenge 2022.
