NISQA：一种用于众包数据集多维语音质量预测的深度CNN-自注意模型
NISQA: A Deep CNN-Self-Attention Model for Multidimensional Speech Quality Prediction with Crowdsourced Datasets
摘要：
在本文中，我们提出了一个更新的NISQA语音质量预测模型，重点是在通信网络中发生的失真。与之前的版本相比，该模型是端到端训练的，时间依赖性建模和时间池是通过自注意机制实现的。除了整体语音质量外，该模型还预测了四个语音质量维度：噪声、着色、不连续性和响度，从而更深入地了解质量下降的原因。此外，还创建了包含13，000多个语音文件的新数据集，用于模型的训练和验证。该模型最终在一个新的现场通话测试数据集上进行了测试，该数据集包含真实电话录音。总体而言，NISQA在来自不同来源的81个数据集上进行了训练和评估，并显示出对未知语音样本也提供了可靠的预测。代码、模型权重和数据集都是开源的。
英文摘要：
In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks. In contrast to the previous version, the model is trained end-to-end and the time-dependency modelling and time-pooling is achieved through a Self-Attention mechanism. Besides overall speech quality, the model also predicts the four speech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness, and in this way gives more insight into the cause of a quality degradation. Furthermore, new datasets with over 13,000 speech files were created for training and validation of the model. The model was finally tested on a new, live-talking test dataset that contains recordings of real telephone calls. Overall, NISQA was trained and evaluated on 81 datasets from different sources and showed to provide reliable predictions also for unknown speech samples. The code, model weights, and datasets are open-sourced.
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。
