A Novel pattern recognition model for real-time voice data input

The classical front end analysis in speech recognition is a spectral analysis which parameterizes the speech signal into feature vectors. This paper proposes a voice recognition model that is able to automatically classify and recognize a voice signal with background noise. The model uses the concept of spectrogram, pitch period, short time energy, zero crossing rate, mel frequency scale and cepestral coefficient in order to calculate feature vectors. The k-Nearest Neighbor (k-NN) classification is used for classification and recognition of real-time input signal. Analytical hierarchical process is used for deciding the weightage of different features.