Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction
Open accessAbstract
Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.
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
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?
Similar study by Arruti et al. showed mean accuracy of 80.05% emotion recognition rate in Basque and 74.82% in Spanish.
“Similar study by Arruti et al. showed mean accuracy of 80.05% emotion recognition rate in Basque and a 74.82% in Spanish [42].”
From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
(1992) · Cited by 23,431 · see citing works
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382 · see citing works
Instance-Based Learning Algorithms
Aha, D. W., Kibler, D. F., & Albert, M. K. (1991) · Machine Learning · doi:10.1023/a:1022689900470 · Cited by 5,217 · see citing works
Ekman, P. (1976) · Medical Entomology and Zoology · Cited by 4,345 · see citing works
Emotion recognition in human-computer interaction
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001) · IEEE Signal Processing Magazine · doi:10.1109/79.911197 · Cited by 2,603 · see citing works
Irrelevant Features and the Subset Selection Problem
John, G. H., Kohavi, R., & Pfleger, K. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50023-4 · Cited by 2,403 · see citing works
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334 · see citing works
Feature Selection for Knowledge Discovery and Data Mining
Liu, H., & Motoda, H. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5689-3 · Cited by 2,216 · see citing works
Vocal affect expression: A review and a model for future research.
Scherer, K. R. (1986) · Psychological Bulletin · doi:10.1037/0033-2909.99.2.143 · Cited by 1,768 · see citing works
A Bio‐Informational Theory of Emotional Imagery
Lang, P. J. (1979) · Psychophysiology · doi:10.1111/j.1469-8986.1979.tb01511.x · Cited by 1,750 · see citing works
Steps toward Artificial Intelligence
Minsky, M. (1961) · Proceedings of the IRE · doi:10.1109/jrproc.1961.287775 · Cited by 1,706 · see citing works
Baluja, S. (1994) · Cited by 1,309 · see citing works
A Branch and Bound Algorithm for Feature Subset Selection
Narendra, & Fukunaga (1977) · IEEE Transactions on Computers · doi:10.1109/tc.1977.1674939 · Cited by 1,266 · see citing works
From recombination of genes to the estimation of distributions I. Binary parameters
Mühlenbein, H., & Paaß, G. (1996) · Lecture notes in computer science · doi:10.1007/3-540-61723-x_982 · Cited by 1,203 · see citing works
Harik, G. R., Lobo, F. G., & Goldberg, D. E. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797971 · Cited by 986 · see citing works
Scherer, K. R., & Ekman, P. (1984) · Medical Entomology and Zoology · Cited by 886 · see citing works
A Survey of Optimization by Building and Using Probabilistic Models
Pelikán, M., Goldberg, D. E., & Lobo, F. G. (2002) · Computational Optimization and Applications · doi:10.1023/a:1013500812258 · Cited by 761 · see citing works
Emotion recognition from speech: a review
Koolagudi, S. G., & Rao, K. S. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-011-9125-1 · Cited by 649 · see citing works
Dellaert, F., Polzin, T., & Waibel, A. (1996) · International Conference on Spoken Language Processing (ICSLP) · doi:10.1109/icslp.1996.608022 · Cited by 570 · see citing works
The Equation for Response to Selection and Its Use for Prediction
Mühlenbein, H. (1997) · Evolutionary Computation · doi:10.1162/evco.1997.5.3.303 · Cited by 515 · see citing works
Tao, J., & Tan, T. (2005) · Lecture notes in computer science · doi:10.1007/11573548_125 · Cited by 511 · see citing works
The production and recognition of emotions in speech: features and algorithms
Pierre-Yves, O. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(02)00141-6 · Cited by 439 · see citing works
Scherer, K. R., Johnstone, & Klasmeyer, G. (2002) · Handbook of Affective Sciences · doi:10.1093/oso/9780195126013.003.0023 · Cited by 426 · see citing works
Emotion recognition in human–computer interaction
Fragopanagos, N., & Taylor, J. G. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.006 · Cited by 414 · see citing works
An empirical study of machine learning techniques for affect recognition in human–robot interaction
Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006) · Pattern Analysis and Applications · doi:10.1007/s10044-006-0025-y · Cited by 313 · see citing works
Data Mining Using MLC a Machine Learning Library in C
Kohavi, R., Sommerfield, D., & Dougherty, J. (1997) · Cited by 298 · see citing works
EMOTION IN SPEECH: RECOGNITION AND APPLICATION TO CALL CENTERS
Petrushin, V. A. (1999) · Cited by 277 · see citing works
Liu, H., & Setiono, R. (1998) · Applied Intelligence · doi:10.1023/a:1008363719778 · Cited by 246 · see citing works
Speech emotion recognition using hidden Markov models
Rodríguez, A. N., Moreno, A., Bonafonte, A., & Acebal, J. B. M. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-627 · Cited by 235 · see citing works
Speech Emotion Recognition Using Support Vector Machine
Pan, Y., Shen, P., & Shen, L. (2012) · Cited by 212 · see citing works
Speech emotion recognition approaches in human computer interaction
Ramakrishnan, S., & Emary, I. M. M. E. (2011) · Telecommunication Systems · doi:10.1007/s11235-011-9624-z · Cited by 187 · see citing works
Automatic Speech Emotion Recognition using Support Vector Machine
Shen, P., Zhou, C., & Chen, X. (2011) · doi:10.1109/emeit.2011.6023178 · Cited by 184 · see citing works
Structure of the Basque emotion lexicon
Alonso‐Arbiol, I., Shaver, P. R., Fraley, R. C., Oronoz, B., Erdozia, E. U., & Urizar, R. (2006) · Cognition & Emotion · doi:10.1080/02699930500405469 · Cited by 165 · see citing works
Emotional space improves emotion recognition
Tato, R., Santos, R. M., Kompe, R., & Pardo, J. M. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-557 · Cited by 159 · see citing works
Automatic discrimination between laughter and speech
Truong, K. P., & van Leeuwen, D. A. (2007) · Speech Communication · doi:10.1016/j.specom.2007.01.001 · Cited by 156 · see citing works
Automatic emotion recognition using prosodic parameters
Luengo, I., Navas, E., Hernáez, I., & Sánchez, J. (2005) · Interspeech (USB) · doi:10.21437/interspeech.2005-324 · Cited by 144 · see citing works
Beyond emotion archetypes: Databases for emotion modelling using neural networks
Cowie, R., Douglas‐Cowie, E., & Cox, C. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.002 · Cited by 130 · see citing works
Feature Selection for Case-Based Classification of Cloud Types: An Empirical Comparison
Aha, D. W., & Bankert, R. L. (1994) · Cited by 130 · see citing works
Shami, M., & Verhelst, W. (2007) · Speech Communication · doi:10.1016/j.specom.2007.01.006 · Cited by 129 · see citing works
A study of distance-based machine learning algorithms
Dietterich, T. G., & Wettschereck, D. (1994) · Cited by 124 · see citing works
Gharavian, D., Sheikhan, M., Nazerieh, A., & Garoucy, S. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0643-1 · Cited by 97 · see citing works
Cross linguistic interpretation of emotional prosody
Abelin, Å., & Allwood, J. S. (2002) · Cited by 89 · see citing works
Data Mining Using $\mathcal{MLC}++$ a Machine Learning Library in C++
Kohavi, R., Sommerfield, D., & Dougherty, J. (1997) · International Journal of Artificial Intelligence Tools · doi:10.1142/s021821309700027x · Cited by 88 · see citing works
ASR for emotional speech: Clarifying the issues and enhancing performance
Athanaselis, T., Bakamidis, S., Dologlou, I., Cowie, R., Douglas‐Cowie, E., & Cox, C. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.03.008 · Cited by 88 · see citing works
An Exact Probability Metric for Decision Tree Splitting and Stopping
Martin, J. K. (1997) · Machine Learning · doi:10.1023/a:1007367629006 · Cited by 72 · see citing works
Context-Independent Multilingual Emotion Recognition from Speech Signals
Hozjan, V., & Kačič, Z. (2003) · International Journal of Speech Technology · doi:10.1023/a:1023426522496 · Cited by 58 · see citing works
ANALYSIS AND MODELLING OF EMOTIONAL SPEECH IN SPANISH
Montero, J. M., Gutiérrez-Arriola, J. M., & Colas, J. T. (1999) · Cited by 52 · see citing works
Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features
Koolagudi, S. G., & Krothapalli, S. R. (2012) · International Journal of Speech Technology · doi:10.1007/s10772-012-9150-8 · Cited by 48 · see citing works
Recognition of emotion in a realistic dialogue scenario
Huber, R., Batliner, A. M., Buckow, J., Nöth, E., Warnke, V., & Niemann, H. (2000) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2000-165 · Cited by 46 · see citing works
Vocal Expression of Emotion Discrete-emotions and Dimensional Accounts
Laukka, P. (2004) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 42 · see citing works
Assistive Technology and Affective Mediation
Garay-Vitoria, N., Cearreta, I., López, J. M., & Fajardo, I. (2006) · Human Technology · doi:10.17011/ht/urn.2006159 · Cited by 39 · see citing works
Self-citationDimensionality reduction-based spoken emotion recognition
Zhang, S., & Zhao, X. (2011) · Multimedia Tools and Applications · doi:10.1007/s11042-011-0887-x · Cited by 29 · see citing works
‘Emotion and brain: Understanding emotions and modelling their recognition’
Taylor, J. G., Scherer, K. R., & Cowie, R. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.04.001 · Cited by 27 · see citing works
Caballero‐Morales, S. (2013) · The Scientific World JOURNAL · doi:10.1155/2013/162093 · Cited by 24 · see citing works
Álvarez, A., Cearreta, I., López, J. M., Arruti, A., Lazkano, E., Sierra, B., & Garay-Vitoria, N. (2006) · Lecture notes in computer science · doi:10.1007/11846406_71 · Cited by 17 · see citing works
Self-citationObtaining and Evaluating an Emotional Database for Prosody Modelling in Standard Basque
Navas, E., Hernáez, I., Castelruiz, A., & Luengo, I. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30120-2_50 · Cited by 15 · see citing works
de Córdoba, R., Montero, J. M., Gutiérrez-Arriola, J. M., Vallejo, J. A., Enríquez, E., & Pardo, J. M. (2002) · Computer Speech & Language · doi:10.1006/csla.2002.0190 · Cited by 14 · see citing works
Speech Emotions Recognition Using 2-D Neural Classifier
Partila, P., & Vozn̆ák, M. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-00542-3_23 · Cited by 13 · see citing works
Validating a Multilingual and Multimodal Affective Database
López, J. M., Cearreta, I., Fajardo, I., & Garay-Vitoria, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73289-1_49 · Cited by 8 · see citing works
Self-citationAcoustical Analysis of Emotional Speech in Standard Basque for Emotions Recognition
Navas, E., Hernáez, I., Castelruiz, A., Sánchez, J., & Luengo, I. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30463-0_48 · Cited by 7 · see citing works
Álvarez, A., Cearreta, I., López, J. M., Arruti, A., Lazkano, E., Sierra, B., & Garay-Vitoria, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74628-7_55 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 10
- [11]Picard RW (1998) Towards Agents that Recognize Emotion. In: Proceedings IMAGINA, pp. 153–165.
- [23]Iriondo I, Guaus R, Rodríguez A, Lázaro P, Montoya N, et al. (2000) Validation of an acoustical modelling of emotional expression in Spanish using speech synthesis techniques. In: Proceedings of the ISCA Workshop on Speech and Emotion, pp. 161–166.
- [25]López JM, Cearreta I, Garay N, López de Ipiña K, Beristain A (2006) Creación de una base de datos emocional bilingüe y multimodal. In: Proceedings of the 7th Spanish Human Computer Interaction Conference, pp. 55–66.
- [28]Hozjan V, Kačič Z (2003) Context-independent multilingual emotion recognition. International Journal of Speech Technology 6(3): 311– 320.
- [30]Tickle A (2000) English and Japanese speaker’s emotion vocalizations and recognition: a comparison highlighting vowel quality. In: Proceedings of the ISCA Workshop on Speech and Emotion, pp. 104–109.
- [39]Schröder M (2004) Speech and emotion research: an overview of research frameworks and a dimensional approach to emotional speech synthesis (Ph.D thesis). Saarland University, Institute of Phonetics.
- [44]Batliner A, Fisher K, Huber R, Spilker J, Nöth E (2000) Desperately Seeking Emotions: Actors, Wizards, and Human Beings. In Proceedings of the ISCA Workshop on Speech and Emotion, pp. 195–200.
- [55]Ting KM (1995) Common issues in Instance-Based and Naive-Bayesian classifiers (Ph.D. Thesis). The Univesity of Sidney Basser, Department of Computer Science.
- [59]Minsky M (1961) Steps towards artificial intelligence. Proceedings of the IRE 49: 8– 30.
- [68]Larrañaga P, Etxeberria R, Lozano JA, Sierra B, Inza I, et al. (1999) A review of the cooperation between evolutionary computation and probabilistic graphical models. In: Proceedings of the II Symposium on Artificial Intelligence (CIMAF99), pp. 314–324.
Cited by
Sorted by citations
Survey on AI-Based Multimodal Methods for Emotion Detection
Marechal, C., Mikołajewski, D., Tyburek, K., Prokopowicz, P., Bougueroua, L., Ancourt, C., & Węgrzyn-Wolska, K. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-16272-6_11 · Cited by 137 · see citing works
Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
Zvarevashe, K., & Olugbara, O. O. (2020) · Algorithms · doi:10.3390/a13030070 · Cited by 72 · see citing works
YogeshC, K., Hariharan, M., Ngadiran, R., Adom, A., Yaacob, S., & Polat, K. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.013 · Cited by 48 · see citing works
Unsupervised feature selection and NMF de-noising for robust Speech Emotion Recognition
Bandela, S. R., & Kumar, T. K. (2020) · Applied Acoustics · doi:10.1016/j.apacoust.2020.107645 · Cited by 37 · see citing works
High-Performance Modelling and Simulation for Big Data Applications
Kołodziej, J., & González–Vélez, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-16272-6 · Cited by 37 · see citing works
Álvarez, A., Sierra, B., Arruti, A., López, J. M., & Garay-Vitoria, N. (2015) · Sensors · doi:10.3390/s16010021 · Cited by 29 · see citing works
Self-citationMachine learning in emotional intelligence studies: a survey
Dollmat, K. S. B., & Abdullah, N. A. (2021) · Behavior and Information Technology · doi:10.1080/0144929x.2021.1877356 · Cited by 15 · see citing works
Cunningham, P. B., Gilmore, J., Naar, S., Preston, S. D., Eubanks, C. F., Hubig, N., McClendon, J. L., Ghosh, S., & Ryan-Pettes, S. R. (2023) · Clinical Child and Family Psychology Review · doi:10.1007/s10567-023-00451-6 · Cited by 21 · see citing works
Duville, M. M., Alonso-Valerdi, L. M., & Ibarra-Zárate, D. I. (2021) · Data · doi:10.3390/data6120130 · Cited by 15 · see citing works
Speech Emotion Recognition using Unsupervised Feature Selection Algorithms
Bandela, S. R., & Kumar, T. K. (2020) · Radioengineering · doi:10.13164/re.2020.0353 · Cited by 14 · see citing works
Emotional Speech Recognition Based on the Committee of Classifiers
Kamińska, D. (2019) · Entropy · doi:10.3390/e21100920 · Cited by 11 · see citing works
Duville, M. M., Alonso-Valerdi, L. M., & Ibarra-Zárate, D. I. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.626146 · Cited by 8 · see citing works
Machine Learning for Video Action Recognition: A Computer Vision Approach
Labayen, M., Aginako, N., Sierra, B., Olaizola, I. G., & Flórez, J. (2018) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2018.00110 · Cited by 6 · see citing works
Self-citationImproving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework
Bakshi, A., & Kopparapu, S. K. (2021) · SN Computer Science · doi:10.1007/s42979-021-00750-1 · Cited by 4 · see citing works
Bakshi, A., & Kopparapu, S. K. (2021) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v10i5.3173 · Cited by 2 · see citing works
Berkai, C., Hariharan, M., Yogesh, C. K., Yaacob, S., & Omar, M. I. (2017) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2017.2022 · Cited by 1 · see citing works
A Multilingual System Based on Semantic Knowledge for Complex Environments
de Ipiña, K. L., Barroso, N., Calvo, P. M., Ezeiza, A., & Hernández, C. (2018) · IEEE International Work Conference on Bioinspired Intelligence (IWOBI) · doi:10.1109/iwobi.2018.8464207 · Cited by 1 · see citing works
BEET: Behavior Engagement Emotion Trigger – A AI-based model to maximize audience engagement
Badawy, W. M. (2021) · Menoufia Journal of Electronic Engineering Research · doi:10.21608/mjeer.2021.193089
Machine learning algorithms in natural language processing for improved human-computer interaction
Arifin, Y., & Soeharyadi, M. C. (2024) · International Journal of Communication and Information Technology · doi:10.33545/2707661x.2024.v5.i1a.75
A Survey on Data Selection for Efficient Speech Processing
Azeemi, A. H., Qazi, I. A., & Raza, A. A. (2025) · IEEE Access · doi:10.1109/access.2025.3582395
Cunningham, P. B., Gilmore, J., Naar, S., Preston, S. D., Eubanks, C. F., Hubig, N., McClendon, J. L., Ghosh, S., & Ryan-Pettes, S. R. (2025) · Gezinstherapie Wereldwijd · doi:10.1007/s12440-025-00258-7
- Authors
- Arruti, A., Cearreta, I., Álvarez, A., Lazkano, E., & Sierra, B.
- Affiliations
- University of the Basque Country (ES)
- Vicomtech (ES)
- Corresponding author
- Arruti, Andoni
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-10-03
- Volume
- 9
- Issue
- 10
- Pages
- e108975
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4184843
- OpenAlex
- W1979374416
- Cited by
- 21
- References
- 64
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2021: 6 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 2 · 2015: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Communities in ADDI (Universidad del Pais Vasco) (submittedVersion, other-oa)
- Communities in ADDI (University of the Basque Country) (submittedVersion, cc-by)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Hispana (submittedVersion, other-oa)
- Funders
- Eusko Jaurlaritza (UFI11/45)
- Euskal Herriko Unibertsitatea (UFI 11/45)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- speech emotion recognition, feature selection, Basque language, Spanish language, instance-based learning, evolutionary algorithms, greedy forward selection, human-computer interaction
- MeSH terms
- Machine Learning, Algorithms, Emotions, Humans, Pattern Recognition, Automated, Speech, User-Computer Interface
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
