采用音质特征和VLAD编码的新冠肺炎检测算法

COVID-19 Detection Algorithm Using Voice Quality Features and VLAD Coding

2020年,世界卫生组织宣布COVID-19疫情为大流行病。为了实现COVID-19快速地、可靠地检测,本研究通过语音信号分析技术来寻找感染COVID-19的语音信号特征,利用咳嗽声片段和语音片段对是否感染COVID-19做出自动判断。在INTERSPEECH 2021 ComParE竞赛提供的相关数据集和baseline的基础上,本文首先利用语音端点检测技术对数据集进行增广,其次在特征集中加入语音质量特征,使相关baseline结果得到了提升,证明了语音质量特征在对COVID-19自动语音检测任务上的有效性。同时,引入局部聚合描述子向量对低级别特征进行编码,当字典大小较小时,有效地提升了系统的分类性能。最后,对多种算法得到的分类结果进行融合,进一步提升分类效果,最终在两个子任务中的验证集上UAR分别取得了73.9%和77.2%。

In 2020,the World Health Organization declared the COVID-19 outbreak a pandemic.In order to promote the rapid and reliable detection of COVID-19,this research introduced voice signal processing technology to find the voice signal characteristics of COVID-19 infection,and automatically judges whether it is infected with COVID-19 using cough fragments and speech fragments.On the basis of the relevant data set and baseline provided by INTERSPEECH 2021 ComParE,firstly,the audio segmentation technology was used to augment the data set.And secondly,voice quality features were added to the feature set,which improved baseline results and proved that the voice quality features are effective on the task of automatic speech detection for COVID-19.At the same time,Vector of Locally Aggregated Descriptors is introduced to encode low-level features.When the dictionary size is small,the classification performance of the system is effectively improved.Finally,the classification results obtained by multiple algorithms are fused to further improve the final classification effect.The UAR for CCS and CSS sub-challenges are 73.9%and 77.2%,respectively.