Class-level spectral features for emotion recognition-Reference-Cited by-同舟云学术

Class-level spectral features for emotion recognition

Published:2010-07 Issue:7-8 Volume:52 Page:613-625
ISSN:0167-6393
Container-title:Speech Communication
language:en
Short-container-title:Speech Communication

Author:

Bitouk Dmitri,Verma Ragini,Nenkova Ani

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modelling and Simulation,Software

Reference41 articles.

1. Acoustic profiles in vocal emotion expression;Banse;J. Personality Social Psychology,1996

2. Bitouk, D., Nenkova, A., Verma, R., 2009. Improving emotion recognition using class-level spectral features. In: Proc. Interspeech 2009.

3. Praat, a system for doing phonetics by computer;Boersma;Glot Internat.,2001

4. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B., 2005. A database of german emotional speech. In: Proc. Interspeech 2005, pp. 1–4.

5. Chang, C.-C., Lin, C.-J., 2001. LIBSVM: a library for support vector machines. Software available at .

Cited by 135 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring emotion detection in Kashmiri audio reviews using the fusion model of CNN, LSTM, and RNN: gender-specific speech patterns and performance analysis;International Journal of Information Technology;2024-08-08

2. In-depth investigation of speech emotion recognition studies from past to present –The importance of emotion recognition from speech signal for AI–;Intelligent Systems with Applications;2024-06

3. Disruptive situation detection on public transport through speech emotion recognition;Intelligent Systems with Applications;2024-03

4. Deep temporal clustering features for speech emotion recognition;Speech Communication;2024-02

5. Combined CNN LSTM with attention for speech emotion recognition based on feature-level fusion;Multimedia Tools and Applications;2024-01-02