# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 3 Method  
The StarGAN v2 [38] image-to-image translation model creates a mapping between several visual domains with a sufficient amount of variation in the output images and scalability over many domains using a single discriminator and generator. Using the same architecture as StarGAN v2, StarGANv2-VC [12] converts voices by treating each speaker as a separate domain. In our work, we employed the same architecture as StarGANv2-VC [12] for emotional voice conversion and considered each of the following four emotions as a separate domain: neutral, happiness, sadness, and anger. Figure 1 presents an overview of the starGANv2-VC framework, which contains a generator and a discriminator in addition to the F0 network, mapping network, and style encoder. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Language** & \begin{tabular}{c} **Selected** \\ **Emotions** \\ \end{tabular} & **Model** \\ \hline [PERSON] et al. [15] & 2016 & English & N, H, F, S & DBLSTM \\ \hline [PERSON] et al. [17] & 2017 & Japanese & \multirow{2}{*}{N, H, S, A} & \begin{tabular}{c} ANNs and \\ DBNs \\ \end{tabular} \\ \hline [PERSON] et al. [20] & 2019 & Japanese & \multirow{2}{*}{N, H, S, A} & DSAN \\ \hline [PERSON] et al. [30] & 2019 & English & N, H, S, A & Autoencoders \\ \hline [PERSON] et al. [35] & 2019 & French & J, F, S, A & Seq2 seq \\ \hline [PERSON] et al. [33] & 2019 & English & N, H, S, A & \begin{tabular}{c} Highway neural \\ network \\ \end{tabular} \\ \hline [PERSON] et al. [26] & 2020 & English & N, H, S, A & StarGAN \\ \hline [PERSON] et al. [2] & 2020 & Chinese & N, S, A & CycleGAN \\ \hline [PERSON] et al. [36] & 2020 & Chinese & N, S, A & MTEVC \\ \hline [PERSON] et al. [28] & 2020 & English & N, A & VAW-GAN \\ \hline [PERSON] et al. [23] & 2020 & English & N, S, A, Sur & CycleGAN \\ \hline [PERSON] et al. [24] & 2020 & English & N, H, S, A & VCGAN \\ \hline [PERSON] et al. [31] & 2020 & English & N, H, S, A & VAE-GAN \\ \hline [PERSON] et al. [8] & 2020 & English & N, H, S, A & FHVAE \\ \hline [PERSON] et al. [16] & 2020 & \begin{tabular}{c} German, Telugu, \\ and English \\ \end{tabular} & N, H, A, F & Hybrid network \\ \hline [PERSON] and [PERSON] [19] & 2021 & Korean & N, H, S, A & Seq2 seq \\ \hline [PERSON] et al. [7] & 2021 & Japanese & N, H, A, J & StarGAN-VC \\ \hline [PERSON] et al. [34] & 2021 & English & N, H, S, A, Sur & Seq2 seq \\ \hline [PERSON] et al. [29] & 2021 & English & N, A, Sl & VAW-GAN \\ \hline [PERSON] et al. [37] & 2021 & \begin{tabular}{c} English and \\ Mandarin \\ \end{tabular} & N, H, S, A & VAW-GAN \\ \hline [PERSON] et al. [32] & 2021 & \begin{tabular}{c} German and \\ English \\ \end{tabular} & \
\begin{tabular}{c} Excited and \\ Disappointed \\ \end{tabular} &
\begin{tabular}{c} Close to \\ (VAW-GAN) \\ \end{tabular} \\ \hline N\(-\)Neutral, H\(-\)Happy, F\(-\)Fear, S\(-\)Sad, A\(-\)Anger, J\(-\)Joy, Sur\(-\)Surprise, Sl\(-\)Sleepy, FHVAE\(-\)factorized hierarchical variational autoencoder, DSAN\(-\)dual supervised adversarial networks, VCGAN\(-\)variational cycleGAN, Seq2 seq\(-\)sequence-to-sequence. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the previous studies. The generator \(G\) receives an input mel-spectrogram \(X_{scr}\) and the fundamental frequency in \(h_{f0}\) is converted into output (\(X_{scr}\), \(h_{str}\), \(h_{f0}\)) reflective of a domain-specific style code \(h_{str}\) either by the style encoder or the mapping network. The primary objective of the **F0 network** is to extract the \(h_{f0}\) from a mel-spectrogram input, while the **mapping network** generates a style vector for domain-specific style code and shares for all domains until the last layer is accessed with a random latent code. The latent coding is sampled from a Gaussian distribution to generate diverse style representations across all domains. The **style encoder** extracts the style code for a domain-specific style code and shares it across all domains.