A probabilistic feature based SVM model for Hindi/English speech recognition

Real time speech recognition has various challenges including noise, turbulence, language and crosstalk problem. In this paper, multi-phase hybridization is applied to cover these challenges and to provide effective speech recognition. The model is explicitly divided into three main stages where each stage is implicitly divided into several sub-stages to provide specific problem solution. The proposed hybrid model resolved the problem of acoustic turbulence, background noise and instrumentation noise problem at the earlier stage. The rectified speech signals are processed using ICA and Fuzzy-HMM approach to generate the structural and statistical features. In this stage, the signal is divided in smaller linear blocks to extract the features. Later on, fuzzy-weighted SVM is implied to recognize the speech signal. The experimentation is applied on Hindi and English characters and sentence datasets. The comparative results are derived against BPNN and PCA models for different sample sets. The comparative results obtained from model signifies that the model has improved the recognition rate effectively.