Devices controlled with voice commands have gained popularity over the last decade. To recognize an utterance, they usually require an internet connection, or use commercial programming libraries. Therefore, their flexibility is low, and algorithm update opportunities are limited. In this study, a speaker independent isolated word recognition algorithm, embedded in a single board computer, is proposed to recognize utterances in real-time. The proposed system neither requires an internet connection, nor uses external libraries. Mel Frequency Cepstral Coefficients and their deltas are used as feature vectors. Gaussian mixture models are utilized to define word models. Digits and some confirmation words of Turkish language are recorded ten times in one session from twenty-four individuals. Seven of these records are used for training, and the others for testing the system. The off-line experimental results showed that the system is working with 99.98%. In real-time experiments, the system's recognition accuracy was proficient for controlled environments.