In this paper, we introduce SPQER (pronounced speaker), a novel approach to evaluate the quality of experience for real-time Voice over IP (VoIP) communication in mobile and lossy networks. Traditional speech quality metrics, e.g., Perceptual Evaluation of Speech Quality (PESQ) or the Hearing-Aid Speech Quality Index (HASQI), directly compare frequencies and amplitudes to calculate the received signal distortions. SPQER instead uses machine learning classification to evaluate the percentage of recognizable words in conjunction with a time-based decay function to penalize delay and cross-talking. So instead of evaluating noise, SPQER directly answers the question: What percentage of words is the recipient able to understand? We presented a sensitivity analysis, which is based on testbed experiments for different packet loss rates and simulated delays, to asses the impact of challenging link conditions. A final correlation analysis to a short user study shows that SPQER can better evaluate the amount of understandable words than PESQ and HASQI, while still giving a more precise indication about the voice quality than the Word Error Rate (WER) metric.

中文翻译：

---

SPQER：在有损和移动网络中使用单词识别进行VoIP通信的语音质量评估

在本文中，我们介绍SPQER（发音为发音者），这是一种评估移动和有损网络中实时IP语音（VoIP）通信的体验质量的新颖方法。传统语音质量度量标准（例如，语音质量的感知评估（PESQ）或助听器语音质量指数（HASQI））直接比较频率和幅度以计算接收到的信号失真。SPQER而是使用机器学习分类来评估可识别单词的百分比，并结合基于时间的衰减函数来惩罚延迟和串扰。因此，SPQER无需评估噪音，而是直接回答以下问题：接收者能够理解多少百分比的单词？我们进行了敏感性分析，这是基于针对不同丢包率和模拟延迟的测试平台实验，以评估具有挑战性的链路条件的影响。一份简短的用户研究的最终相关分析表明，与PESQ和HASQI相比，SPQER可以更好地评估可理解单词的数量，同时仍比单词错误率（WER）指标更准确地指示语音质量。