用于预测真实世界信号的众包语音质量评级的金字塔递归网络
A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals
摘要：
客观语音质量测量的真实世界能力是有限的，因为当前的测量（1）是从模拟数据开发的，不能充分模拟真实环境;或者它们（2）预测的客观分数并不总是与主观评级强烈相关。此外，目前还不存在具有收听者质量评级的真实世界信号的大型数据集，这将有助于促进真实世界的评估。在本文中，我们收集和预测由人类听众评估的真实世界语音信号的感知质量。我们首先通过对两个真实语料库进行众包听力研究来收集大型质量评级数据集。我们进一步开发了一种新的方法，预测人类的质量评级使用金字塔双向长短期记忆（pBLSTM）网络与注意力机制。结果表明，该模型实现了统计上较低的估计误差比以前的评估方法，其中预测的分数与人类的判断强烈相关。
英文摘要：
The real-world capabilities of objective speech quality measures are limited since current measures (1) are developed from simulated data that does not adequately model real environments; or they (2) predict objective scores that are not always strongly correlated with subjective ratings. Additionally, a large dataset of real-world signals with listener quality ratings does not currently exist, which would help facilitate real-world assessment. In this paper, we collect and predict the perceptual quality of real-world speech signals that are evaluated by human listeners. We first collect a large quality rating dataset by conducting crowdsourced listening studies on two real-world corpora. We further develop a novel approach that predicts human quality ratings using a pyramid bidirectional long short term memory (pBLSTM) network with an attention mechanism. The results show that the proposed model achieves statistically lower estimation errors than prior assessment approaches, where the predicted scores strongly correlate with human judgments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
