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Speech-to-speech translation systems are very important in bridging the communication gap across different language / cultural divides. The first problem in these systems is to translate the words properly and in addition to this, there is the problem of emotions and tones in the language. This research proposes an approach that focuses on the translation quality of the speech to speech translation system and adds sophisticated emotion perception to the system. The proposed system enhances accuracy by using state-of-art machine learning to enhance translation accuracy in capturing the details of the language in use or the context of the text being translated. This involves word-embeddings that allow machines to capture the semantics of words, and therefore offer translations that are not only literal but also contextual. Also, the incorporation of an emotion detector is an innovation, as it determines the emotional state of the speaker and keeps it in the translated speech. This improvement is beneficial to the flow and naturalness of the translated text, as well as to the overall realism of interaction. The efficiency of the system is proved by the comprehensive assessment of its work. They demonstrate that working with the proposed system allows achieving high translation accuracy while preserving the emotional component of the speech. From the study it is possible to conclude that there is great potential for enhancing cross-linguistic communication through the use of this integrated approach. In this way, the system improves the quality of relations between people by keeping the emotional aspect in the forefront and being useful in various communication contexts.