On the Evaluation of the Conversational Speech Quality in Telecommunications

On the Evaluation of the Conversational Speech Quality in Telecommunications
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电信会话语音质量评价研究

DOI:
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发表时间:
2008
影响因子:
1.9
通讯作者:
Vincent Barriac
Vincent Barriac
中科院分区:
工程技术4区
文献类型:
--
作者:
M. Guéguin;R. Bouquin;Valérie Gautier;G. Faucon;Vincent Barriac

文献摘要

被引文献

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我们提出了一种客观的方法,通过考虑说话和听语音质量以及延迟的影响来评估会话环境下的语音质量。该方法应用于四种主观测试的结果,即回波、延迟、丢包和噪声的影响。数据集分为训练集和验证集。对于训练集,应用多元线性回归来确定会话、说话和听力语音质量与延迟值之间的关系。在训练集和验证集上,多元线性回归可以准确估计会话得分,主观得分和估计得分之间具有高相关性和低误差。此外,对文献中发现的主观检验数据进行验证,证实了回归的可靠性。然后,将这种关系应用于客观水平,用现有客观模型提供的说话和听力客观分数取代说话和听力主观分数,并在主观测试期间记录语音信号。通过与测试结果和ITU-T(国际电信联盟)建议G.107中提出的现有标准方法“E-model”进行比较,可以发现对话模型实现了高性能。
We propose an objective method to assess speech quality in the conversational context by taking into account the talking and listening speech qualities and the impact of delay. This approach is applied to the results of four subjective tests on the effects of echo, delay, packet loss, and noise. The dataset is divided into training and validation sets. For the training set, a multiple linear regression is applied to determine a relationship between conversational, talking, and listening speech qualities and the delay value. The multiple linear regression leads to an accurate estimation of the conversational scores with high correlation and low error between subjective and estimated scores, both on the training and validation sets. In addition, a validation is performed on the data of a subjective test found in the literature which confirms the reliability of the regression. The relationship is then applied to an objective level by replacing talking and listening subjective scores with talking and listening objective scores provided by existing objective models, fed by speech signals recorded during the subjective tests. The conversational model achieves high performance as revealed by comparison with the test results and with the existing standard methodology "E-model," presented in the ITU-T (International Telecommunication Union) Recommendation G.107.