Neural network speaker dependent isolated Malay speech recognition system: handcrafted vs genetic algorithm
Md Sah Hj Salam, Dayangku Nur Faizah Pengiran Mohamad, S.H.S. Salleh · 2002
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.