95498481-f898-4e99-9d00-132ecc5670a820250718115136638naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037131202513120251910.46300/9109.2025.19https://npublications.com/journals/educationinformation/2025.phpCostin AndreiBratanPolitehnica University of Bucharest, RomaniaAndreeaMarinescuUniversity of Medicine and Pharmacy „Carol Davila”, Bucharest, RomaniaElenaTerecoasaUniversity of Medicine and Pharmacy „Carol Davila”, Bucharest, RomaniaAna VoichitaTebeanuPolitehnica University of Bucharest, RomaniaBogdanMorosanuPolitehnica University of Bucharest, RomaniaEduardFrantiResearch Institute for Artificial Intelligence “Mihai Drăgănescu”, Bucharest, RomaniaMonicaDascaluPolitehnica University of Bucharest, RomaniaAlexandraAndreiPolitehnica University of Bucharest, RomaniaClaudiaTocila-MataselMedima Health, Otopeni, RomaniaBogdanIonescuPolitehnica University of Bucharest, RomaniaGheorgheIanaMedima Health, Otopeni, RomaniaAna MariaOproiuUniversity of Medicine and Pharmacy „Carol Davila”, Bucharest, RomaniaGabrielaIorgulescuUniversity of Medicine and Pharmacy „Carol Davila”, Bucharest, RomaniaThe process of inner speaking, often referred to as self-talk or inner monologue, holds significant importance in the artistic expression of young student actors during their early and inexperienced new performances. This paper draws upon various studies and employs three distinct techniques to elucidate the underlying mechanisms of inner speaking's role in emotional expression and highlights its implications for a young student actor's training and performance enhancement. The first method involves the utilization of magnetic resonance imaging (MRI) to observe the oxygenated parts of the brain that are associated with emotional discharge. This approach is conducted by a team of medical imaging specialists and utilizes a 3.0 T Signa Pioneer MRI scanner. The second method employs the use of a convolutional neural network with fully connected layers that have been trained using Mel-frequency cepstral coefficients (MFCCs) of the input audio files. This technique is used to capture the strength of emotion expressed in the speech of student actors on the first day of training relative to the final day of trial. 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