TY - GEN
T1 - Hilbert-Huang Mel Frequency Cepstral Coefficient for Speech Stress Recognition System
AU - Prasetio, Barlian Henryranu
AU - Syauqy, Dahnial
AU - Widasari, Edita Rosana
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Today, acoustic and spectral characteristics are commonly utilized to determine stress levels, and they have a high degree of accuracy. Mel frequency cepstral coefficients are the most successful spectral characteristic (MFCCs). On MFCC framework, each window was determined using a Fourier transformation based on its resolution. The window's size, on the other hand, causes frequency resolution issues, particularly for under-stressed speech, since the frequency of each speech can change due to emotional conditions. Hence, it is necessary to analyze the frequency spectrum over time and has an adaptive window. To address this issue, we apply Hilbert-Huang transform into MFCC (called HFCC) feature extraction technique for more robust stress speech recognition system. The extracted features are then used as trained data using neural network (NN) to identify the emotional stress of speaker. We used actual speech stress data from SUSAS Database in all experiments. The experimental result shows that HFCC outperforms MFCC and the existing feature extraction techniques.
AB - Today, acoustic and spectral characteristics are commonly utilized to determine stress levels, and they have a high degree of accuracy. Mel frequency cepstral coefficients are the most successful spectral characteristic (MFCCs). On MFCC framework, each window was determined using a Fourier transformation based on its resolution. The window's size, on the other hand, causes frequency resolution issues, particularly for under-stressed speech, since the frequency of each speech can change due to emotional conditions. Hence, it is necessary to analyze the frequency spectrum over time and has an adaptive window. To address this issue, we apply Hilbert-Huang transform into MFCC (called HFCC) feature extraction technique for more robust stress speech recognition system. The extracted features are then used as trained data using neural network (NN) to identify the emotional stress of speaker. We used actual speech stress data from SUSAS Database in all experiments. The experimental result shows that HFCC outperforms MFCC and the existing feature extraction techniques.
KW - Feature extraction technique
KW - Hilbert-huang Transform
KW - Machine Learning
KW - MFCC
KW - Stress Speech
UR - https://www.scopus.com/pages/publications/85141825601
U2 - 10.1109/ICITACEE55701.2022.9923955
DO - 10.1109/ICITACEE55701.2022.9923955
M3 - Conference contribution
AN - SCOPUS:85141825601
T3 - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
SP - 111
EP - 114
BT - Proceedings - 2022 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
A2 - Prakoso, Teguh
A2 - Riyadi, Munawar Agus
A2 - Arfan, M.
A2 - Soetrisno, Yosua Alvin Adi
A2 - Afrisal, Hadha
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2022
Y2 - 25 August 2022 through 26 August 2022
ER -