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Data Fusion based on Game Theory for Speaker Diarization

机译:基于博弈论的说话人差异化数据融合

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A novel algorithm based on bimatrix game theory has been developed to improve the accuracy and reliability of a speaker diarization system. This algorithm fuses the output data of two open-source speaker diarization programs, LJUM and SHoUT, taking advantage of the best properties of each one. The performance of this new system has been tested by means of audio streams from several movies. From preliminary results on fragments of five movies, improvements of 63% in false alarms and missed speech mistakes have been achieved with respect to LIUM and SHoUT systems working alone. Moreover, we also improve in a 20% the number of recognized speakers, getting close to the real number of speakers in the audio stream.
机译:已经开发了一种基于双矩阵博弈论的新颖算法,以提高说话人二分系统的准确性和可靠性。该算法利用了每个开源扬声器二元化程序LJUM和SHoUT的最佳性能,将它们的输出数据融合在一起。这个新系统的性能已经通过几部电影的音频流进行了测试。从五部电影片段的初步结果来看,与单独使用的LIUM和SHoUT系统相比,错误警报和语音失误的错误率提高了63%。此外,我们还将识别的说话者数量提高了20%,接近音频流中的真实说话者数量。

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