# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
###### Abstract  
Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the \"main speaker voice\" is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications.  
speaker separation; speaker identification; deep learning; VoIP +
Footnote †: journal:  
an example of speech separation implementation that requires real-time processing [4, 5]. Conversely to these applications used in speaker recognition and speaker separation, this research uses both speaker recognition and separation in a different application, which is VoIP.  
Figure 1 illustrates that humans can focus on the voice produced by a single speaker in a crowded and noisy environment where they can perform speech-non-speech separation (de-noising) and speaker separation (multi-speaker talking separation) simultaneously [6]. This simple task for humans has proven extremely challenging to mimic in speech processing systems. As the demand for a system that can perform real-time processing is growing, this research focuses on using both speaker recognition and real-time speech separation models to improve the online call process or (VoIP). Our contributions are: (i) A novel SSI which is an integration of deep learning audio separation and identification models, (ii) A separation test up to four speakers at a time, (iii) Involving this model in a VoIP functional system.  
This paper is organised as follows. Section 2 presents the literature review of speaker identification and speaker separation methods. In Section 3, the proposed model description and the main block diagram of our study are discussed. In Section 4, the setup for conducting our experiment is described. Section 5 presents and discusses the results. Section 6 contains the conclusion.