# Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model
## 1 Introduction  
Human speech is a sophisticated signal that contains significantly more information than lexical words. Only 7% of the message is communicated by spoken words or verbal attitude; 38% is communicated by non-verbal vocal characteristics, which reflect emotional state; 55% is transmitted by facial expression [1]. Non-linguistic information, such as emotional state, plays an essential role in human social interaction [2]. In recent years, interaction with machines has become commonplace in various aspects, such as entertainment, security, and healthcare, due to tremendous technological progress [3; 4; 5]. One of the most critical aspects of human-machine interaction is voice interaction. Despite developments that have been made, many of the current systems cannot express emotion and the resulting voice does not seem natural because of the lack of essential emotions. Voice transformation (VT) intends to modify one or more characteristics of a speech signal without altering its linguistic content. It can be used to change a speaker's speaking style, such as switching from neutral to emotional speech or mimicking a native speaker's accent. VT can also be used to change the identification of the speaker by the voice conversion (VC) approach. VC seeks to modify the speech of a source speaker so that the output is viewed as a sentence spoken by a target speaker [6]. Emotional voice conversion (EVC) is a subset of VT, which refers to converting the emotion from the source utterance to the target utterance without changing the linguistic information or speaker identity [7; 8]. It is necessary for us to convey our intentions, feelings, and social attitudes through the emotional expression that we use in our everyday talking. Therefore, one of the mostimportant aspects of speech synthesis is the generation of speech signals with designated emotional attributes [7]. The interest in virtual assistants has grown due to advances in speech processing systems, dialogue systems, and natural language processing. The synthesis of emotional speech by machines bears enormous application potential, particularly in human-machine communication. People would feel more comfortable if the virtual assistant was emotionally controlled, giving it great commercial and social value. Devices such as Alexa, Cortana, and Siri are currently able to broadly synthesize speech, but not yet in different varieties of emotions [8; 9]. A human-like speech interaction between humans and machines can be achieved through the ability of EVC to control synthetic speech emotion [8]. EVC's primary challenge is the low availability of training data, especially for under-resourced languages such as Arabic. Indeed, speech collection with emotion annotation necessitates the performance of the required emotions by trained specialists. Another difficulty is that people may disagree with the emotion that is being expressed. It is also challenging to assess emotion control mechanisms due to the subjectivity of speech [7; 8]. There is relatively little progress in the field of EVC due to the complexities of emotional features. Deep neural networks have enabled several EVC frameworks to achieve higher performance than frameworks based on conventional models. Recently, generative adversarial networks (GANs), with their different variants (e.g., cycle-consistent generative adversarial network (CycleGAN, StarGANs, etc. ), and autoencoder are the most common techniques used in EVC frameworks [1; 10]. There is a significant gap in the research between Arabic and other more widely disseminated languages such as English and Chinese. We might argue that studies on this topic are extremely rare and scarce, where we found only this study [11] on emotional Arabic voice conversion (A-EVC). To the best of our knowledge, this is the first study on an Arabic emotional voice conversion (A-EVC) using StarGANv2-VC. The lack of Arabic emotional databases, and the scarcity of studies in this field, represent a significant challenge in A-EVC.