Speaker diary compilation by dependent combination of audio coefficients

The paper describes a novel method that improvises the procedure for supervised speaker diary compilation. The procedure supposes that the database of the speakers is available. Initially, the database and observation signal of the speakers, are prepared. The audio features have been extracted from the database and the observation signal. Instead of using one of Mel Frequency Cepstral Coefficient, Perceptual Linear Prediction, or Power Normalized Cepstral Coefficients, a combination of all of them have been used. The combination form of these features is independent, i.e. they are concatenated in the feature matrix. The comparison between features of observation signal and statistical properties of database features, has been made. A comparing procedure is used to make the decision of the logical mask for comparison. Both of bottom-up and top-down scenarios collaborate to complete the last decisions successfully. Diary compilation Error Rate test denotes that combination of features has less errors than any one alone.