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With internet quickly becoming an easy access to many, voice calling overinternet is slowly gaining momentum. Individuals has been engaging in videocommunication across the world in different languages. The decade saw theemergence of language translation using neural networks as well. With more databeing generated in audio and visual forms, there has become a need and achallenge to analyse such information for many researchers from academia andindustry. The availability of video chat corpora is limited as organizationsprotect user privacy and ensure data security. For this reason, an audio-visualcommunication system (VidALL) has been developed and audio-speeches wereextracted. To understand human nature while answering a video call, an analysiswas conducted where polarity and vocal intensity were considered as parameters.Simultaneously, a translation model using a neural approach was developed totranslate English sentences to French. Simple RNN-based and Embedded-RNN basedmodels were used for the translation model. BLEU score and target sentencecomparators were used to check sentence correctness. Embedded-RNN showed anaccuracy of 88.71 percentage and predicted correct sentences. A key findingsuggest that polarity is a good estimator to understand human emotion.

Translation, Sentiment and Voices: A Computational Model to Translate and Analyse Voices from Real-Time Video Calling