A neural network-based text independent voice recognition system

Abstract

A text-independent voice recognition experiment was conducted using an artificial neural network. The speech data were collected from three different speakers uttering thirteen different words. Each word was repeated ten times. The speech data were then pre-processed for signal conditioning. A total of 12 feature parameters were obtained from Cepstral coefficients via a linear predictive coding (LPC). These feature parameters then served as inputs 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 was tested for the remaining unseen words in text-independent mode. The results were very promising with a voice recognition accuracy of more than 90%. The success rate could be increased by adding more utterances from each speaker.<>