In this context, a study of Arabic lemmas frequency was conducted to identify the highly frequent lemmas that often occur in written and spoken Classical and Modern Standard Arabic (MSA). To this end, a lemma-based approach for concatenative TTS synthesis is adopted and presented in this paper. The primary intention of this work is to increase the quality of the produced speech resulting from the sub-segment based approach proposed in our previous work. Nevertheless, the most of the available free and semi-free Arabic Text To Speech systems are still away from the natural sounding as human voice does, and the generation of smooth voice is still involved. Indeed, many researchers are still investigating in Arabic Text To Speech to deliver an intelligible and close to. Therefore, it has gained the interest of researchers in speech technologies in particular speech recognition and speech synthesis. Recent numbers put The Arabic language at around 250 million native speakers, making it the fifth spoken language regarding the number of speakers. Text to Speech synthesis, Deep learning, Tacotron 2, WaveNet This model consists of a recurrent sequence-to-sequence prediction feature network that maps embedding characters to mel-spectrograms, followed by a modified WaveNet model that acts as a vocoder to synthesize time-domain waveforms from those spectrograms. The model we used is Tacotron 2 end-to-end speech synthesis, which has a neural network architecture for speech synthesis directly from the text. To overcome this problem we develop a prototype for Amharic Text to Speech synthesis using Deep learning a subset of machine learning in artificial intelligence. Also, language is dynamically changed day to day and invented new words so detecting those words dynamically is not an easy task.

However, the written text of a language contains both standard words and non-standard words like numbers, abbreviations, synonyms, currency, and dates. These technologies have been developed for decades. Text to Speech synthesis is converting natural language text into speech. AI-powered engines are crucial to the success of multimedia platforms, and thus the paper introduces AI-powered two-way integration architecture. The interdependence diagram and brand voice life cycle reveal that AI defines brand voice's effectiveness and helps evolve it by offering suggestions. This paper finds that AI plays a crucial role in evolving, developing, predicting, and analyzing brand voice in multimedia, resulting in the current life cycle of the brand voice. By drawing implications from shared study areas, the interdependence of these three notions was determined. Initially, the role of AI in the evolution of brand voice, AI in multimedia, and the role of brand voice in multimedia were reviewed, highlighting the research gap. Qualitative research design is applied based on content analysis of various multimedia applications.

The aim is to explore how AI has evolved brand voice in multimedia and its interdependencies. With digitalization, brand voice has become necessary in brand communication with users, and conversational AI interprets inputs. Digital automation and artificial intelligence (AI) have transformed over decades as more organizations communicate with audiences utilizing multimedia platforms globally.

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