지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.9
- 수록면
- 214 - 222 (9page)
- DOI
- 10.5370/KIEEP.2023.72.3.214
이용수
이 논문의 연구 히스토리 (3)
2023
- 이 논문의 후속연구가 궁금하신가요?
- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
- 이 논문의 연구 히스토리 확인하기
초록· 키워드
오류제보하기
Speech emotion recognition is a technology that identifies emotional states in human speech and plays a crucial role in enhancing Human Computer Interactions (HCI) more naturally and effectively. This technology supports the accurate understanding and appropriate response to human emotions through AI. In this study, we compare and analyze the performance of a speech emotion recognition model based on deep learning that utilizes the fusion of multiple features from speech signals. Various features such as bark-spectrum, mel-spectrum, Mel Frequency Cepstrum Coefficient (MFCC), and GammaTone Cepstrum Coefficient (GTCC) are extracted from the speech signal. Among these, two types of features are fused based on Canonical Correlation Analysis (CCA) methods to obtain a new single feature vector, which is then used as the input for the one-dimensional Convolutional Neural Network (1D-CNN) emotion recognition model. This fused feature contributes to enhancing the efficiency and accuracy of emotion recognition, and its performance is analyzed in comparison to the case of using only each feature. The performance of the model is evaluated using the AI-hub emotion classification dataset and the Korean speech emotion state classification dataset constructed by Chosun University. For both datasets, the multiple features level fusion through the CCA method improved the performance of voice emotion recognition compared to using single features.
목차
Abstract
1. 서론
2. CCA기반 다중특징융합을 이용한 음성 감정 인식
3. 실험 및 결과 분석
5. 결론
References
참고문헌 (19)
참고문헌 신청
P. Gangamohan, S.R. Kadiri and B. Yegnanarayana, “Analysis of Emotional Speech—A Review,” Toward Robotic Socially Believable Behaving Systems, vol. I, pp. 205-238, Mar. 2016.
R. A. Khalil, E. Jones, M. I. Babar, T. Jan, M. H. Zafar and T. Alhussain, “Speech Emotion Recognition Using Deep Learning Techniques: A Review,” in IEEE Access, vol. 7, pp. 117327-117345, Aug. 2019.
J. D. Lope, M. Graña, “An ongoing review of speech emotion recognition,” Neurocomputing, vol. 528, pp.1-11, Apr. 2023.
T. Anvarjon, Mustaqeem, and K. Soonil, “Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features,” Sensors vol. 20, no. 18, pp. 1-16, Sep. 2020.
P. Jiang, H. Fu, H. Tao, P. Lei and L. Zhao, “Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition,” in IEEE Access, vol. 7, pp. 90368-90377, Jul. 2019.
R. A. Khalil, E. Jones, M. I. Babar, T. Jan, M. H. Zafar and T. Alhussain, “Speech Emotion Recognition Using Deep Learning Techniques: A Review,” in IEEE Access, vol. 7, pp. 117327-117345, Aug. 2019.
J. D. Lope, M. Graña, “An ongoing review of speech emotion recognition,” Neurocomputing, vol. 528, pp.1-11, Apr. 2023.
T. Anvarjon, Mustaqeem, and K. Soonil, “Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features,” Sensors vol. 20, no. 18, pp. 1-16, Sep. 2020.
P. Jiang, H. Fu, H. Tao, P. Lei and L. Zhao, “Parallelized Convolutional Recurrent Neural Network With Spectral Features for Speech Emotion Recognition,” in IEEE Access, vol. 7, pp. 90368-90377, Jul. 2019.
이 논문의 저자 정보
이 논문과 함께 이용한 논문
CNN-based Speech Emotion Recognition Model Applying Transfer Learning and Attention Mechanism
이정현
,
윤의녕
,
조근식
Journal of KIISE
2020
.07
Analysis of Speech Emotion Database and Development of Speech Emotion Recognition System using Attention Mechanism Integrating Frame- and Utterance-level Features
김도경
,
김윤중
Journal of KIISE
2020
.05
감성기반 서비스를 위한 통화 음성 감정인식 기법
방재훈
,
이승룡
정보과학회논문지 : 소프트웨어 및 응용
2014
.03
자연어 처리와 음성 인식을 활용한 기상-AI 검색기에 관한 연구
김병연
,
고수완
,
김태종
외 7명
한국기상학회 학술대회 논문집
2024
.04
Wav2vec 특징 기반의 한국어 음성감정인식
안영도
,
한상욱
,
이성주
외 1명
한국통신학회 학술대회논문집
2021
.11
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0102-2023-560-002034721