Translating the sign of dumb person using ARM processor

The goal of the blabbering voice-to-Speech Translation research is to enable real-time, interpersonal communication via natural spoken language for people who do not share a common language. The Multilingual Automatic blabbering voice-to-Speech Translator (MASTOR) system is the first Speech-to-Speech system that allows for bidirectional (blabbering voice Tamil) free-form speech input and output. The speech interactivity embedded module (SIEM) that is quite simplified and suitable for programmable anthropomorphic dialogue and menu driven recognition applications. The proposed SIEM adopts Average Magnitude Different Function (AMDF)-based pitch feature extractor and Dynamic Time Warping (DTW)-based recognizer. The simulated experiments indicate the performance is improved considerably relative to the conventional LPCC-based DTW method. In addition, we apply the proposed SIEM design for wireless master/slave architecture for mobile consumer devices, providing a cost effective solution to meet the request of real-time and flexibility in embedded system prototype development. It also encourages concurrent development of different parts of system hardware as well as module reusing.