有损转码语音的自动说话人验证评估

Jozef Polacky, R. Jarina, M. Chmulik
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引用次数: 9

摘要

本文研究了有损语音压缩对文本无关说话人验证任务的影响。我们已经评估了几种最先进的语音编解码器的语音生物识别性能,包括最近发布的增强型语音服务(EVS)编解码器。测试在编解码器匹配和编解码器不匹配两种情况下进行。测试结果表明,EVS优于我们测试中使用的其他语音编解码器,并且可以用于生成对不同压缩级别具有相当鲁棒性的扬声器模型。当训练数据中包含更高质量的语音编解码器(EVS, G711)(不匹配和部分不匹配场景)时,自动说话人验证(ASV)的结果优于匹配场景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of automatic speaker verification on lossy transcoded speech
In this paper, we investigate the effect of lossy speech compression on text-independent speaker verification task. We have evaluated the voice biometrics performance over several state-of-the art speech codecs including recently released Enhanced Voice Services (EVS) codec. The tests were performed in both codec-matched and codec-mismatched scenarios. The test results show that EVS outperforms other speech codecs used in our test and it can be used to generate speaker models that are quite robust to varying compression levels. It was also shown that if a speech codec of higher quality (EVS, G711) is included in training data (mismatched and partially mismatched scenarios), the automatic speaker verification (ASV) gives better results than in the case of matched scenario.
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