# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 2 Literature Review  
The majority of speaker identification techniques used in the literature before deep learning models were based on i-vector techniques [7, 8, 9]. These techniques look for patterns in audio signals and categorise them using methods such as the Mel Frequency Cepstral Coefficient (MFCC), which is essential for identifying speakers. Alternatives to MFCC are provided, including Linear Predictor Coefficient (LPC), Line Spectral Frequency (LPF), Rhythm, Chroma Factor, Turbulence, Spectrum Sub-band Centroid (SSC), and other feature lists. The most frequently employed model for training on our data is the Gaussian Mixture Model (GMM). Other related models, such as the Model of Hidden Markov Chains, can also be used for training (HMM). Recently, the majority of the model training phase for a speaker identification project was carried out using Artificial Neural Networks (ANN). When compared to many traditional approaches, deep learning algorithms have significantly improved the state of the speech separation topic in recent years [10, 11, 12, 13, 14, 15]. A basic neural network speech separation approach begins by applying the short-time  
Figure 1: Humans can focus on the target speech signal in a crowded and noisy environment [6]. Fourier transform (STFT) to the combined sound to obtain a representation of time and frequency (T-F). After that, inverse STFT is used to synthesise the source waveforms from the T-F bins associated with each source. This concept raises several concerns. First, it is debatable whether Fourier decomposition is the best way to convert the signal for speech separation. Secondly, the signal's amplitude and phase must be taken into account by the separation algorithm because STFT converts the signal into a complex domain. Because altering the phase is difficult, the bulk of proposed approaches merely adjust the STFT's magnitude by generating a time-frequency mask for each source and synthesising using the masked magnitude spectrogram with the mixture's initial phase. As a result, separation performance is constrained above. Even though numerous techniques, such the phase-sensitive mask [16] and complex ratio mask [17], have been developed to leverage phase information to create the masks, because the reconstruction is inaccurate, the upper bound remains. Furthermore, for effective speech separation in the STFT domain, high-frequency resolution is required, resulting in relatively long time windows, which are typically greater than 32 ms for speech [12; 13; 14] and greater than 90 ms for music separation [18]. Such systems cannot be used in situations where extremely low latency is required, such as in hearable devices or telecommunication systems because the minimal latency of the system is constrained by the size of the STFT time window. Directly modelling the signal in the time-domain is a straightforward technique to get around these challenges. The method in [19] indicated recent success in tasks including voice recognition, speech synthesis, and speech improvement [20; 21; 22; 23; 24]. The current leading approach [19], which is based on an overcomplete set of linear filters, divides the filter outputs at each time step using a mask for two speakers or a multiplexer for more speakers. The audio is then rebuilt using these incomplete representations. Because the order of the speakers is assumed to be random, one uses a permutation invariant loss during training so that the permutation that minimizes the loss is taken into account (it is difficult to sort sounds). This masking-based approach has certain limitations because as the number of speakers increases, the mask must extract and suppress more information from the representation, making it more difficult to deal with the partial representations stated before. Thus, a mask-free approach that was introduced in [25] is used.