Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

Abstract

Purpose:

Select the right features on the frame for good accuracy

Design/methodology/approach:

Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) Features

Findings/result:

The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.

Originality/value/state of the art:

Selection of the appropriate features on the frame.