Evaluation of soft segment modeling on a phoneme recognition system

In this paper, soft segment model has been evaluated and optimized on a phoneme recognition system in various aspects and the concept of soft segment recognition has been developed as an optimum recognition algorithm for this model. The main idea of this recognition method is consideration of the neighbor segments effects in recognition phase in the same way that has been applied in the training phase. The weighing procedure of feature vectors is modeled as repetitions of the vectors with frequencies less than one. Comparing the results of this model with classic models, it has been shown that there is an impressive improvement in both recognition rate and time at the optimum overlap coefficient. The selection between hard or soft recognition methods is a trade off between recognition rate and time. Comparing with standard CDHMM, a drastic decrease in recognition time and a little increase in recognition rate have been observed in the achieved results, which it shows more compatibility of the proposed model with the speech nature in comparison with other models.