Search based applications for speech processing

Nowadays, the demand for human-to-machine communication systems by using human voice is constantly growing. These systems allow the user to get access to information, fundament decisions and solve daily life or business issues in a timely and natural manner, with as little effort as possible. The complexity of the user input requested by many front-end speech recognition and back-end processing systems leads to imminent barriers when it comes to fulfilling a request. The main contribution of this paper consists in evaluating speech recognition solutions that support search engine integration. Furthermore, we propose an architecture for an intelligent speech-to-process platform using semantic analysis of natural language for processing the back-end orientations that can greatly benefit from search engine technologies.