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Voicing-aware parametric speech quality models over VoIP networks

机译:VoIP网络上可发声的参数语音质量模型

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This paper describes novel parametric speech quality models which subsume the effect of packet loss distribution and voicing feature of missing signal waves. Speech quality estimate models for voiced and unvoiced loss location patterns are developed following multiple statistical regression analysis of measurements gathered from a built speech quality assessment framework. The overall speech quality is estimated by combining voiced and unvoiced speech quality estimate scores using an expression calibrated using a large number of speech samples. The input parameters namely, mean loss durations and ratios for voiced and unvoiced packets, of speech quality estimate models are extracted at run-time using a new voicing-aware packet loss Markov model. This chain, calibrated at run-time, finely models bursty packet loss behavior over voiced and unvoiced missing speech waves. Performance evaluation study shows that our voicing-aware speech quality estimate models clearly outperform voicing-agnostic speech quality models in terms of accuracy over a wide range of conditions.
机译:本文描述了新颖的参量语音质量模型,该模型考虑了丢包分布和丢失信号波的发声特征的影响。在对从已建语音质量评估框架中收集到的测量值进行多次统计回归分析之后,针对浊音和清音位置模式的语音质量估计模型进行了开发。通过使用使用大量语音样本校准的表达式将浊音和清音语音质量估计值进行组合,可以估计整体语音质量。语音质量估计模型的输入参数,即平均丢失持续时间和有声包和无声包的比率,是在运行时使用新的有声觉的包丢失马尔可夫模型提取的。该链在运行时进行了校准,可以对有声和无声的丢失语音波的突发数据包丢失行为进行精确建模。绩效评估研究表明,在多种条件下,我们的可识别语音的语音质量估计模型在准确度方面明显优于可识别语音的语音质量模型。

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