  Speaker recognition is an active research area that contains notable usage in
biometric security and authentication system. Currently, there exist many
well-performing models in the speaker recognition domain. However, most of the
advanced models implement deep learning that requires GPU support for real-time
speech recognition, and it is not suitable for low-end devices. In this paper,
we propose a lightweight text-independent speaker recognition model based on
random forest classifier. It also introduces new features that are used for
both speaker verification and identification tasks. The proposed model uses
human speech based timbral properties as features that are classified using
random forest. Timbre refers to the very basic properties of sound that allow
listeners to discriminate among them. The prototype uses seven most actively
searched timbre properties, boominess, brightness, depth, hardness, roughness,
sharpness, and warmth as features of our speaker recognition model. The
experiment is carried out on speaker verification and speaker identification
tasks and shows the achievements and drawbacks of the proposed model. In the
speaker identification phase, it achieves a maximum accuracy of 78%. On the
contrary, in the speaker verification phase, the model maintains an accuracy of
80% having an equal error rate (ERR) of 0.24.

Sound;Artificial Intelligence;Machine Learning