A dynamic segment based statistical derived PNN model for noise robust Speech Recognition

Speech Recognition has proven its significance in various online and offline applications including the authentication systems, translators, voice commander, etc. But as the voice is captured from some instrument it suffers from various impurities because of technical faults and environmental disturbance. These all noise criticalities degrade the accuracy of speech recognition methods. In this paper, a more robust and reliable method is presented for offline speech recognition applied to letters and words. At the early stage of this model, a hybrid method is provided to achieve the noise robust speech modeling. This modeling is applied to filter the speech signal and remove the signal errors. Later on, a dynamic feature driven segmented model is applied to transform the speech signal in statistical data. This statistical data values are finally trained under probabilistic neural network to perform the speech recognition for testing speech signal. The implementation is applied on Hindi and English speech datasets generated from raw primary sources. The comparative analysis is obtained against neural network and SVM approaches applied to the raw speech signal dataset. The results show that the work model has provided the accuracy improvement up to 15% for different data sets and approaches.