Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 176

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Recognition of emotions in interactive voice response systems

Yacoub, Sherif, Simske, Steve, Lin, Xiaofan, Burns, John·2003·8th European Conference on Speech Communication and Technology (Eurospeech 2003)

Abstract

This paper reports emotion recognition results from speech signals, with particular focus on extracting emotion features from the short utterances typical of Interactive Voice Response (IVR) applications. We focus on distinguishing anger versus neutral speech, which is salient to call center applications. We report on classification of other types of emotions such as sadness, boredom, happy, and cold anger. We compare results from using neural networks, Support Vector Machines (SVM), K-Nearest Neighbors, and decision trees. We use a database from the Linguistic Data Consortium at University of Pennsylvania, which is recorded by 8 actors expressing 15 emotions. Results indicate that hot anger and neutral utterances can be distinguished with over 90% accuracy. We show results from recognizing other emotions. We also illustrate which emotions can be clustered together using the selected prosodic features.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21437/eurospeech.2003-307

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 1 references

Cited by

See all 176

Metadata

Authors
Yacoub, S., Simske, S., Lin, X., & Burns, J.
Venue
8th European Conference on Speech Communication and Technology (Eurospeech 2003)
Publisher
ISCA
Published
2003-09-01
Pages
729-732
OpenAlex
W1535925594
Cited by
176
References
1
FWCI
20.54
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: Recognition of emotions in interactive voice response systems. https://app.cochat.ai/papers/10.21437/eurospeech.2003-307
The paper
Yacoub, S., Simske, S., Lin, X., & Burns, J. (2003). Recognition of emotions in interactive voice response systems. 8th European Conference on Speech Communication and Technology (Eurospeech 2003). https://doi.org/10.21437/eurospeech.2003-307

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.