Effects of the Dynamic and Energy Based Feature Extraction on Hindi Speech Recognition

Abstract

Background: Speech Recognition is the most effective and suitable way of communication. Extracted features play an important role in speech recognition. Previous research works for Hindi speech recognition lack detailed comparative analysis of the feature extraction methods using dynamic and energy parameters. Objective: The research work presents experimental work done to explore the effects of integrating dynamic coefficients and energy parameters with different feature extraction techniques on Connected word Hindi Speech recognition. As extracted features play a significant role in speech recognition, a comparative analysis is presented to show the effects of integration of dynamic and energy parameters to basic extracted features. Method: Speaker dependent system was proposed with monophones based five states Hidden Markov Model (HMM) using HTK Tool kit. Speech data set of connected words in Hindi was created. The feature extraction techniques such as Linear Predictive Coding Cepstral coefficients (LPCCs), Mel Frequency Cepstral Coefficients (MFCCs), and Perceptual Linear Prediction (PLPs) coefficients were applied integrating delta, delta2, and energy parameters to evaluate the performance of the proposed methodology for speaker dependent recognition. Results: Experimental results show that the system achieved the highest recognition word accuracy of 89.97% using PLP coefficients. The PLP coefficients achieved 4% increment in word accuracy than original MFCCs and a 16% increment in word accuracy than LPCCs. Adding energy parameters to original MFCCs increased word accuracy by 1.5% only while adding dynamic coefficients delta and double delta had no significant effect on speech recognition accuracy. Conclusion: Research findings reveal that PLP coefficients outperformed. Explorations reveal that the integration of energy parameters are better than original MFCCs. Investgations also reveal that adding energy parametres improved recognition score while adding delta and delta2 coefficients to basic features did not improve the recognition scores. Research findings could be used to enhance the performance of a speech recognition system by using a suitable feature extraction technique and combining the different feature extraction techniques. Further, investigations can be used to develop language resources for refining speech recognition. The work can be extended to develop a continuous Hindi speech recognition system.