Integrated search technique for parameter determination of SVM for speech recognition

Support vector machine (SVM) has a good application prospect for speech recognition problems; still optimum parameter selection is a vital issue for it. To improve the learning ability of SVM, a method for searching the optimal parameters based on integration of predator prey optimization (PPO) and Hooke-Jeeves method has been proposed. In PPO technique, population consists of prey and predator particles. The prey particles search the optimum solution and predator always attacks the global best prey particle. The solution obtained by PPO is further improved by applying Hooke-Jeeves method. Proposed method is applied to recognize isolated words in a Hindi speech database and also to recognize words in a benchmark database TI-20 in clean and noisy environment. A recognition rate of 81.5% for Hindi database and 92.2% for TI-20 database has been achieved using proposed technique.