Advanced Speech Processing: from Transcription to Emotional Multilingual Synthesis

In today’s world, the communication across any language barriers is very crucial. The aim of this project is to develop an end-to-end emotionally enhanced speech and text translation using deep learning techniques. It is integrated with three components such as Speech-to-Text (STT), Text-to-Speech (TTS), and Neural Machine Translation (NMT). The STT leverages state of the art models like Wav2vec for accurate transcribe spoken language into text. The TTS employs a deep convolution neural network Text-to-Speech system for converting the text back into natural language sounding speech. For language conversion, this system utilizes NMT model like Google’s AudiopaLM, it enables the seamless translation across multiple languages. The proposed system is designed to provide the high-quality translation, and by making it a valuable tool for international industrial business, educational fields, and cultural communications. By the combination of all these technologies into cohesive frameworks, this project will be the potential of deep learning to bridge language gaps.