Sampling-based fuzzy speech clustering systems for faster communication with virtual robotics toward social applications

For social applications, virtual robotics technologies require the Internet of Things (IoT) and cloud services. IoT based-speaker identification is required for virtual social robotics to operate in the most secure manner possible. Moreover, humans communicate using only natural speech to virtual robotics through IoT devices; Virtual robotics are the most emerging and which are required developing the speech-based identification models in the virtual cloud platforms. State of the art of speech models defines the composite technique with the combination of k -means clustering and Gaussian mixture model (GMM) for speaker identification problems. Every speaker has their own annotation, acoustic, and utterance. The Gaussian mixture model (GMM) describes the modeling of features in the speech clustering system for speaker identification applications. These traditional systems are expensive for speech clustering of large datasets when considering the time and space parameters. Sampling-based-fuzzy-spherical k -means and sampling-based-fuzzy-mini-batch k -means (mbkm) are proposed to overcome the problem of scalability issue in the implementation of speech-based virtual cloud-based robotic systems. For demonstrating the effectiveness of the proposed fuzzy-based speaker models, extensive experimental research is conducted using a variety of benchmark speech datasets.