Fuzzy-based voiced-unvoiced segmentation for emotion recognition using spectral feature fusions

Yusnita Mohd Ali, Alhan Farhanah Abd Rahim, Emilia Noorsal, Zuhaila Mat Yassin, Nor Fadzilah Mokhtar, Mohamad Helmy Ramlan

Abstract


Despite abundant growth in automatic emotion recognition system (ERS) studies using various techniques in feature extractions and classifiers, scarce sources found to improve the system via pre-processing techniques. This paper proposed a smart pre-processing stage using fuzzy logic inference system (FIS) based on Mamdani engine and simple time-based features i.e. zero-crossing rate (ZCR) and short-time energy (STE) to initially identify a frame as voiced (V) or unvoiced (UV). Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) were tested with K-nearest neighbours (KNN) classifiers to evaluate the proposed FIS V-UV segmentation. We also introduced two feature fusions of MFCC and LPC with formants to obtain better performance. Experimental results of the proposed system surpassed the conventional ERS which yielded a rise in accuracy rate from 3.7% to 9.0%. The fusion of LPC and formants named as SFF LPC-fmnt indicated a promising result between 1.3% and 5.1% higher accuracy rate than its baseline features in classifying between neutral, angry, happy and sad emotions. The best accuracy rates yielded for male and female speakers were 79.1% and 79.9% respectively using SFF MFCC-fmnt fusion technique.

Keywords


Short-time energy; Zero-crossing rate; Fuzzy logic; Mel-frequency cepstral coefficients; Linear prediction coefficients; Emotion recognition

Full Text:

PDF


DOI: http://doi.org/10.11591/ijeecs.v19.i1.pp196-206

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics