The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition
Abstract
Speech emotion recognition is a challenging yet important speech technology. In this paper, the GMM supervector based SVM is applied to this field with spectral features. A GMM is trained for each emotional utterance, and the corresponding GMM supervector is used as the input feature for SVM. Experimental results on an emotional speech database demonstrate that the GMM supervector based SVM outperforms standard GMM on speech emotion recognition.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Emotion Recognition based on Phoneme Classes
Lee, C. M., Yildirim, S., Bulut, M., Kazemzadeh, A., Busso, C., Deng, Z., Lee, S., & Narayanan, S. (2004) · Interspeech 2004 · doi:10.21437/interspeech.2004-322 · Cited by 228
Cited by
CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset
Cao, H., Cooper, D. G., Keutmann, M. K., Gur, R. C., Nenkova, A., & Verma, R. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2336244 · Cited by 914
Class-level spectral features for emotion recognition
Bitouk, D., Verma, R., & Nenkova, A. (2010) · Speech Communication · doi:10.1016/j.specom.2010.02.010 · Cited by 219
An experimental study of speech emotion recognition based on deep convolutional neural networks
Zheng, W. Q., Yu, J. S., & Zou, Y. X. (2015) · 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344669 · Cited by 187
Audio-visual emotion fusion (AVEF): A deep efficient weighted approach
Ma, Y., Hao, Y., Chen, M., Chen, J., Lu, P., & Košir, A. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.06.003 · Cited by 155
Liu, F., Shen, S.-Y., Fu, Z.-W., Wang, H.-Y., Zhou, A.-M., & Qi, J.-Y. (2022) · Entropy · doi:10.3390/e24071010 · Cited by 21
Metadata
- Authors
- Hu, H., Xu, M.-X., & Wu, W.
- Venue
- 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
- Publisher
- IEEE
- Published
- 2007-04
- Pages
- IV-413-IV-416
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1972280480
- Cited by
- 198
- References
- 1
- FWCI
- 19.32
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition. https://app.cochat.ai/papers/10.1109/icassp.2007.366937
- The paper
- Hu, H., Xu, M.-X., & Wu, W. (2007). GMM Supervector Based SVM with Spectral Features for Speech Emotion Recognition. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07. https://doi.org/10.1109/icassp.2007.366937
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
