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Automatic Speech Recognition using Vector Quantization Concept

Anjali Diwan, Bhargav Ravat


Since even before the time of Alexander Graham Bell’s revolutionary invention, engineers and scientists have studied the phenomenon of speech communication with an eye on creating more efficient and effective systems of human-to-human and human-to-machine communication digital signal processing (DSP), assumed a central role in speech studies. The first step is the extraction of feature vectors based on MFCC. The second is the classification of feature vectors using Vector quantization. The extracted acoustic parameters from the voice signals are used as an input for the MFCC. The main advantage of this method is less computation time and possibility of real-time system development. This paper introduces the design and implementation of the system for recognizing pathological and normal voice. In this ASR system we have used Vector quantization Algorithm.


Mel Frequency Cepstral Coefficient (MFCC), Acoustic Parameters, Speech Processing, Vector Quantization, ASR, Hamming Window, Feature Extraction

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