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Why teo mfcc is better?

Feature (machine learning)

Feature extraction

Convolutional neural network

Speaker recognition

Pattern recognition (psychology)

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The Teager Energy Operator (TEO) combined with Mel Frequency Cepstral Coefficients (MFCC) as Teager-MFCC (T-MFCC) is shown to be better due to its effectiveness in recognizing stressed emotions and energy-based emotions in speech signals. T-MFCC features have demonstrated superior performance in emotion recognition tasks compared to traditional MFCC features. This improvement is evident in various studies where T-MFCC achieved higher classification accuracies for both male and female speakers . Additionally, in speaker verification tasks, the fusion of TEO phase with MFCC features has been found to enhance the accuracy of the system significantly, outperforming MFCCs alone under various conditions . The utilization of TEO in conjunction with MFCC brings complementary information that contributes to the overall effectiveness of the system.

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Papers (5)	Insight
Open access•Proceedings Article•DOI Music Instrument Classification using Nontonal MFCC Yi Wu, Qi Wang, Ruolun Liu - Show less +2 more 01 Apr 2017 1 Citations	NMFCC outperforms MFCC in music instrument classification due to its ability to distinguish timbre differences more clearly using nontonal spectral content, resulting in higher accuracy rates.
Proceedings Article•DOI Fusion of TEO Phase with MFCC Features for Speaker Verification Purvi Agrawal, Hemant A. Patil - Show less +1 more 26 Feb 2015 2 Citations	Fusion of TEO phase with MFCC improves Speaker Verification accuracy by 3.10% over MFCC alone, indicating TEO phase complements MFCC features for enhanced performance in speaker recognition tasks.
Open access•Book Chapter Speech emotion recognition using feature fusion of TEO and MFCC on multilingual databases Syed Asif Ahmad Qadri, Teddy Surya Gunawan, Mira Kartiwi, Hasmah Mansor - Show less +3 more 01 Jul 2020 2 Citations	TEO is superior to MFCC in speech emotion recognition due to its better recognition rate. The fusion of TEO and MFCC as T-MFCC enhances energy-based emotion classification.
Proceedings Article•DOI Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN) Muhammad Syafiq Nordin, Ani Liza Asnawi, Nur Aishah Bt. Zainal, Rashidah Funke Olanrewaju, Ahmad Jusoh, S. Noorjannah Ibrahim, Nor Fadhillah Mohamed Azmin - Show less +6 more 14 Nov 2022	Teager-MFCC (T-MFCC) is superior due to its ability to capture nonlinear speech components affected by stress, resulting in higher classification accuracies of 95.83% (male) and 95.37% (female).
Proceedings Article•DOI Stress Detection based on TEO and MFCC speech features using Convolutional Neural Networks (CNN) 14 Nov 2022	The fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC) as Teager-MFCC (T-MFCC) captures nonlinear speech components, enhancing stress detection accuracy in CNN classification.

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