The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Exploring the benefits of discretization of acoustic features for speech emotion recognition
Open accessAbstract
We present a contribution to the Open Performance subchallenge of the INTERSPEECH 2009 Emotion Challenge.We evaluate the feature extraction and classifier of EmoVoice, our framework for real-time emotion recognition from voice on the challenge database and achieve competitive results.Furthermore, we explore the benefits of discretizing numeric acoustic features and find it beneficial in a multi-class task.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
MFCC and spectral features are good candidates for speech emotion recognition.
“In our previous studies [32], MFCC and spectral features turned out to be good candidates.”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [32]
Sections 3, 3.4 and 3.5 include MFCCs and spectral features among the features used for speech emotion recognition. Section 3.9 says the authors selected relevant features from the larger set, but the passages do not establish that MFCC and spectral features specifically turned out to be good candidates. (LLM, conf=0.79)
Experimental — accuracy not yet measuredThe feature set slightly exceeded the organizers' baseline on the FAU Aibo Emotion Corpus for a five-class problem.
“On the FAU Aibo Emotion Corpus [30], as part of the INTERSPEECH Emotion Challenge 2009 [25], we were able to slightly exceed the baseline given by the organizers for a five class problem (anger, emphatic, neutral, positive, and rest) [32].”
From Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data (2011) · cites this paper as [32]
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
The INTERSPEECH 2009 emotion challenge
Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009 · doi:10.21437/interspeech.2009-103 · Cited by 938
Flagged first, then by citations
Research works printed but not linked: 12
- S. W. Gilroy, M. Cavazza, R. Chaignon, S.-M. Mäkelä, M. Nira- nen, E. André, T. Vogt, J. Urbain, M. Billinghurst, H. Seichter, and M. Benayoun, "E-tree: emotionally driven augmented reality art," in Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, BC, Canada: ACM, 2008, pp. 945-948.
- C. Jones and J. Sutherland, "Acoustic emotion recognition for affective computer gaming," in Affect and Emotion in Human- Computer Interaction, ser. LNCS, C. Peter and R. Beale, Eds. Heidelberg, Germany: Springer, 2008, vol. 4868.
- T. Vogt, E. André, and N. Bee, "EmoVoice -A framework for online recognition of emotions from voice," in Proceedings of Workshop on Perception and Interactive Technologies for Speech- Based Systems. Kloster Irsee, Germany: Springer, June 2008.
- S. Casale, A. Russo, G. Scebba, and S. Serrano, "Speech emotion classification using machine learning algorithms," in 2008 IEEE International Conference on Semantic Computing, Santa Clara, CA, USA, August 2008, pp. 158-165.
- A. Batliner, J. Buckow, R. Huber, V. Warnke, E. Nöth, and H. Nie- mann, "Boiling down prosody for the classification of boundaries and accents in german and english," in Proceedings of Eurospeech 2001, Aalborg, Denmark, September 2001, pp. 2781-2784.
- P. Boersma, "Accurate short-term analysis of the fundamental fre- quency and the harmonics-to-noise ratio of a sampled sound," in Proceedings of the Institute of Phonetic Sciences, University of Amsterdam, 1993, pp. 97-110.
- G. Fink, "Developing HMM-based recognizers with ESMER- ALDA," in Lecture notes in Artificial Intelligence, V. Matoušek et al., Eds. Berlin, Heidelberg: Springer, 1999, vol. 1962, pp. 229-234.
- R. J. J. H. van Son and L. C. W. Pols, "An acoustic description of consonant reduction," Speech Communication, vol. 28, no. 2, pp. 125-140, June 1999.
- U. M. Fayyad and K. B. Irani, "Multi-interval discretization of continuousvalued attributes for classification learning," in Thir- teenth International Joint Conference on Articial Intelligence, Chambéry, France, 1993, pp. 1022-1027.
- I. H. Witten and E. Frank, Data Mining: Practical machine learn- ing tools with Java implementations, 2nd ed. San Francisco: Morgan Kaufmann, 2005.
- M. A. Hall, "Correlation-based feature subset selection for ma- chine learning," Master's thesis, University of Waikato, New Zealand, 1998.
- T. Vogt and E. André, "Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition," in Proceedings of International Conference on Multimedia & Expo, Amsterdam, The Netherlands, July 2005.
Cited by
Sorted by citations
Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
Wagner, J., Andre, E., Lingenfelser, F., & Kim, J. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.12 · Cited by 156
Self-citation
Metadata
- Authors
- Vogt, T., & André, E.
- Affiliations
- University of Augsburg (DE)
- ORCID
- André, Elisabeth
- Venue
- Interspeech 2009
- Publisher
- ISCA
- Published
- 2009-09-06
- Pages
- 328-331
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W122166360
- Cited by
- 19
- References
- 1
- Citations by year
- 2019: 1 · 2018: 1 · 2015: 2 · 2014: 2 · 2013: 1 · 2012: 3
- FWCI
- 6.93
- Type
- conference paper
- Language
- en
- Open access
- green
- nbn-resolving.org
- Locations
- Interspeech (USB) (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speech emotion recognition, feature extraction, real-time emotion recognition, voice emotion recognition, multi-class emotion classification
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
