Parametric feature-based voice recognition system using artificial neural network

In this paper, a speaker identification experiment was conducted using an artificial neural network. The speech data were collected from nine different speakers saying the same word ″Hello″. The speech data were then preprocessed for signal conditioning. A total of 14 feature parameters were obtained in which twelve of them are the coefficient of the 12^th order linear predictor (LPC) and the other two were selected as the peak and bandwidth of spectral envelop. These 14 feature parameters then served as the input to the neural network for speaker classification. A standard two-layer feedforward neural network was trained to identify different feature sets associated with the corresponding speakers. The network size was selected to be 14-8-4 (14 input, 8 hidden and 4 output units). Nine (9) utterances from each speaker were used as training data and the other one served as testing data. No pagination in original publication.