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EN
In recent years, speech recognition functionality is increasingly being added in embedded devices. Because of limited resources in these devices, there is a need to assess whether the defined speech recognition system is feasible within given constraints, as well as estimating how many resources the system needs. In this paper, an attempt has been taken to define a technique for estimating hardware resources usage in the speech recognition task. To determine the parameters and their dependencies in this task, the two systems were tested. The first system utilized Dynamic Time Warping pattern matching technique, the second used Hidden Markov Models. For each case, the measurement of recognition rate and time, vocabulary database size and learning time has been performed. Obtained results have been exploited to define linear and polynomial regression models, and finally, an estimation algorithm has been developed using these models. After testing proposed approach, it was observed that even low-end mobile phones have sufficient hardware resources for realisation of isolated speech recognition system.
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