• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computer speech and language >Evaluating the predictions of objective intelligibility metrics for modified and synthetic speech
【24h】

Evaluating the predictions of objective intelligibility metrics for modified and synthetic speech

机译：评估修改和合成语音的客观清晰度指标的预测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Several modification algorithms that alter natural or synthetic speech with the goal of improving intelligibility in noise have been proposed recently. A key requirement of many modification techniques is the ability to predict intelligibility, both offline during algorithm development, and online, in order to determine the optimal modification for the current noise context. While existing objective intelligibility metrics (OIMs) have good predictive power for unmodified natural speech in stationary and fluctuating noise, little is known about their effectiveness for other forms of speech. The current study evaluated how well seven OIMs predict listener responses in three large datasets of modified and synthetic speech which together represent 396 combinations of speech modification, masker type and signal-to-noise ratio. The chief finding is a clear reduction in predictive power for most OIMs when faced with modified and synthetic speech. Modifications introducing durational changes are particularly harmful to intelligibility predictors. OIMs that measure masked audibility tend to over-estimate intelligibility in the presence of fluctuating maskers relative to stationary maskers, while OIMs that estimate the distortion caused by the masker to a clean speech prototype exhibit the reverse pattern.
机译：最近已经提出了几种用于改变自然或合成语音的修改算法，其目的是提高噪声的清晰度。许多修改技术的关键要求是能够在算法开发过程中离线和在线预测可懂度的能力，以便确定当前噪声上下文的最佳修改。尽管现有的客观清晰度指标（OIM）对于平稳且波动的噪声中未经修改的自然语音具有良好的预测能力，但对于其对其他形式语音的有效性知之甚少。当前的研究评估了七个OIM在修改和合成语音的三个大型数据集中如何预测听众的反应，这三个数据集一起代表了396种语音修改，掩蔽类型和信噪比组合。主要发现是，面对修改后的合成语音，大多数OIM的预测能力明显降低。引入持续变化的修改对清晰度预测器尤其有害。相对于固定掩蔽器而言，在发生掩蔽性波动的情况下，测量掩蔽可听性的OIM往往会高估清晰度，而估计掩蔽器对干净语音原型造成的失真的OIM则呈现相反的模式。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号