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The innovation term has been so widely misused that the confusion observed among the companies trying to get themselves into the innovation realm is a common and natural consequence. The lack of understanding of the innovation dynamics, flow and metrics generally culminate in a non-well-thought implementation of innovation processes and policies that are usually tragic in the short term. The most common consequences are the loss of credibility of the innovation process in general among leaders and employees, and the loss of credibility of the company as an innovative company among suppliers, partners and customers, causing these companies to abandon this powerful tool and, as consequence, to limit their capabilities to compete in the future. In order to prevent this from happening, companies that were not built upon innovation will need to grow capability and change cultural priorities to match the demands of the innovation process.

Although voice recognition is not essential for telematics application, it is certainly one of the most attractive features for the customer who buys it. The appealing is such that may determine the whole system success in the market and, therefore, deserves our attention. This paper discusses the voice recognition task and ways to improve it in the vehicle hostile environment. In order to create a robust system, artificial neural networks are being used in combination to the appropriate signal processing, which includes temporal alignment and spectral transformation for MFCCs features extraction. The problems related to in-vehicle application are described and feasible solutions are suggested. A proposed architecture is then designed considering all foreseen complications, taking advantage of sensors already available in other vehicle systems, ensuring the performance optimization at minimum cost increase.