Speaker Identification Approach for the Post-pandemic Era of Internet of Things

Abstract

With the rapid aggravation of COVID-19 pandemic, the organizations, industries, institutions, etc., are forced to rapidly adapt to social distancing by limiting physical contact and thereby limit the person to person contamination. The scenario of Internet of things in the post COVID era will be interesting indeed. Inorder to ensure public health, the social distancing and semi lockdown will continue in the foreseeable future, and therefore, the need of secure remote person authentication methods is being more and more critical especially in the Internet of things which is a multitude of networks consisting of a huge number of uniquely identifiable devices. As far as human_device authentication strategies are concerned, the one which needs less human involvement will be preferable in a post COVID-19 IoT scenario. Moreover, since the range of IoT devices may span from tiny sensors to complex machines, an authentication method which will be adaptable to each and every type of device will be more welcomed. Considering these facts, voice biometric authentication seems to be the most suitable one which can provide a balanced mix of security, adaptivity and convenience to such an advanced world of connectivity. Here, we introduce a lightweight text independent voice biometric method for IoT using extreme learning machines, and we perform a comparative analysis with a deep learning-based method of speaker identification using 3D convolutional neural networks. We have performed experimental study using different datasets and concluded that the extreme learning-based method is more suitable for IoT, considering the trade-off between the recognition accuracy and the training time requirements.