# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 2 Literature Review  
Earlier studies on voice conversion primarily relied on parallel training data (parallel VC), referring to a pair of utterances with the same content but distinct emotions from the same speaker. Through the paired feature vectors, the conversion model learns a mapping from the source to the target emotion during training [1; 6]. Many parallel VC studies adopt a sequence-to-sequence conversion method and encoder-decoder sequence-to-sequence architecture with an attention mechanism. Spectrum [15], prosody features (F0 and energy contour) [15; 16], spectral features [17; 18], intensity [16], and mel-spectrograms [19] are extracted simultaneously from both the source and the target emotional voice. Deep bidirectional long short-term memory (DBLSTM) [15], artificial neural networks (ANNs), deep belief networks (DBNs) [17], dual supervised adversarial networks [20], recurrent neural networks (RNN), and LSTM are used to propose emotional voice conversion frameworks for German, Telugu, Japanese, and English languages. Many VC techniques (including those mentioned above) are part of the parallel VC method, which uses training data for the parallel vocalization pairs to learn to map. However, this method has two significant drawbacks: first, obtaining data, which is frequently time-consuming or impractical, and second, using time alignment, which most VC methods rely on but which occasionally fails and necessitates other time-consuming operations, such as pre-checking the exact or manual correction [21]. As a solution to the parallel VC issues, a non-parallel VC that refers to the multi-emotion utterances that do not share the same lexical content across emotions [1,6] has been presented. [PERSON] et al. [22] suggested a CycleGAN model for unpaired image-to-image translation that learns without paired samples. Many different non-parallel training data for the emotive VC framework are employed with the CycleGAN schema. [PERSON] et al. [23] performed spectrum and prosody conversion based on CycleGAN, [PERSON] et al. [24] presented a hybrid architecture that depends on a CycleGAN, and [PERSON] et al. [2] examined how effective the use of CycleGAN was for the Chinese emotional VC task. The fundamental frequency energy contour as well as the mel-frequency cepstral coefficients are the features that are utilized to carry out VC tasks [2,23,24]. StarGAN schema was also applied for EVC. [PERSON] et al. [25] used StarGAN, which allows non-parallel many-to-many VC with no parallel utterances, transcriptions, or time-alignment procedures for speech generator training. [PERSON] et al. [26] used a StarGAN similar to that described in [25] to convert real emotional speech samples into various target emotions as a data augmentation method; it was then verified using a multi-class speech affect recognition test. Furthermore, [PERSON] et al. [7] employed the same network architecture used in [25]. They investigated how well StarGAN-VC can achieve non-parallel many-to-many EVC for Japanese phrases when applied only to spectral envelope transformation. [PERSON] et al. [27] developed a non-parallel VC system with a variational autoencoding Wasserstein GANs (VAW-GAN). [PERSON] et al. in [28,29] developed a speaker-independent EVC framework based on VAE-GAN [27]. The main aim was to convert anyone's emotion without needing parallel data. [PERSON] et al. [30] suggested an unsupervised non-parallel emotional speech conversion based on style transfer autoencoders. Their model is simple to use in real-world scenarios and does not require paired data, transcripts, or temporal alignment. In [31], [PERSON] et al. used another non-parallel emotional speech conversion method in the English language based on VAE-GAN by improved CycleGAN. Their results show that the submitted method outperforms their baseline [30].