Neural network speaker dependent isolated Malay speech recognition system: handcrafted vs genetic algorithm

We compare two approaches in selecting neural network learning parameters and architecture. Traditionally they are found by trial and error (handcrafted) and alternatively, can be found using a genetic algorithm. Trial and error can find good solutions but the drawback is this method is time consuming and it can only try a few possible solutions while the genetic algorithm is known to be able to search for a good solution intelligently and faster with greater diversity of possible solutions. We tested the approaches on ten isolated Malay digits from 0 to 9. Three factors are compared between the two approaches: time to get a good solution; network learning convergence; and the recognition rate. Our findings show that the neural network using the genetic algorithm achieved 94% recognition rate while the handcrafted neural network achieved 95%. However, using the genetic algorithm, a good solution can be found within days while with the handcrafted method it took weeks. The network learning convergence for both approaches were relatively the same