A Comparative Study of MFCC and LPCC Features For Speech Activity Detection Using Deep Belief Network

Speech activity detection (SAD) is an important preprocess in speech processing. It identifies intervals of the audio signal with speech from intervals without speech. In this paper speech activity detection is performed using a Deep Belief Network (DBN). The features used as input to the network were either Mel Frequency Cepstral Coefficients (MFCC) or Linear Predictive Cepstral Coefficients (LPCC). The DBN was implemented using MATLAB toolbox DeeBNET V3.0. The training and testing were performed with 13 MFCC features as input and then 12 LPCC features as input. When the testing was performed with speech containing audio data of different signal to noise ratio (SNR) values, the network classified it accurately. The same accuracy was not achieved when the testing data consisted of only stationary noise. The DBN provided near ideal results for SAD when trained with both MFCC and LPCC features.