NDLI: Significance of acoustic features for designing an emotion classification system

Content Provider	IEEE Xplore Digital Library
Author	Kumar, Sudhakar Das, Tushar Kanti Laskar, Rabul Hussain
Copyright Year	2014
Description	Author affiliation: Dept. of Electronics & Communication Engineering, National Institute of Technology Silchar, India (Das, Tushar Kanti; Laskar, Rabul Hussain) \|\| School of Electronics & Communication Engineering, Shri Mata Vaishno Devi University, Jammu and Kashmir, India (Kumar, Sudhakar)
Abstract	This paper reports some of the observations carried out on SUSE database for emotion classification. A comparative study is made to evaluate the performance of Linear Prediction Cepstral Coefficients (LPCCs) and Mel Frequency Cepstral Coefficients (MFCCs) for designing the emotion classification system for word level utterances. The significance of the orders of the coefficients has been carried out during this study. The results obtained using $12^{th}$ order LPCC and $13^{th}$ order MFCC are compared with respect to their reduced dimensions of lower orders. A new classification system based on the feature extraction technique using the $2^{nd},$ $3^{rd}$ and $4^{th}$ order coefficients of both MFCCs and LPCCs is also proposed. This paper compares the accuracy level of both MFCC and LPCC, enabling us to decide which orders of the parameters (both MFCC and LPCC) are more efficient in conveying the emotion for word level utterances. The initial experiments performed at word level utterances reveal that LPCC is more efficient in detecting emotion as compared to MFCC. Further, we noticed that the emotions conveyed in word level utterances are detected more accurately than that in sentence level utterances. The result suggests that word level approach provides better performance for emotion classification as compared to sentence level approach if the system is designed using vocal tract information only.
Starting Page	128
Ending Page	131
File Size	1083594
Page Count	4
File Format	PDF
e-ISBN	9781479941667
DOI	10.1109/ICECE.2014.7026962
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-20
Publisher Place	Bangladesh
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mel frequency cepstral coefficient Speech Databases Feature extraction Emotion recognition Accuracy Normalized Mean Emotion Classification Vocal tract Characteristics LPCC MFCC LP Residual Mean Value Distance
Content Type	Text
Resource Type	Article

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