Speech intelligibility assessment models are essential tools for researchers to evaluate and improve speech processing models. In this study, we propose InQSS, a speech intelligibility assessment model that uses both spectrogram and scattering coefficients as input features. In addition, InQSS uses a multi-task learning network in which quality scores can guide the training of the speech intelligibility assessment. The resulting model can predict not only the intelligibility scores but also the quality scores of a speech. The experimental results confirm that the scattering coefficients and quality scores are informative for intelligibility. Moreover, we released TMHINT-QI, which is a Chinese speech dataset that records the quality and intelligibility scores of clean, noisy, and enhanced speech.

翻译：语言感官评估模型是研究人员评价和改进语言处理模型的基本工具。在本研究中,我们建议使用语言感官评估模型,即使用光谱和分散系数作为输入特征的语音感官评估模型。此外,语言感官评估模型还使用多任务学习网络,高质量分数可以指导语言感官评估的培训。由此产生的模型不仅可以预测语言感官分数,还可以预测演讲的质量分数。实验结果证实,传播系数和质量分数对智能都具有信息意义。此外,我们发布了TMHINT-QI,这是一个中国语音数据集,记录了清洁、吵闹和强化语言的质量和智能分数。