EmoMatchSpanishDB: study of speech emotion recognition machine learning models in a new Spanish elicited database
In this paper we present a new speech emotion dataset on Spanish. The database is created using an elicited approach and is composed by fifty non-actors expressing the Ekman’s six basic emotions of anger, disgust, fear, happiness, sadness, and surprise, plus neutral tone. This article describes how...
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| Published in | Multimedia tools and applications Vol. 83; no. 5; pp. 13093 - 13112 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2024
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 1573-7721 1380-7501 1573-7721 |
| DOI | 10.1007/s11042-023-15959-w |
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| Abstract | In this paper we present a new speech emotion dataset on Spanish. The database is created using an elicited approach and is composed by fifty non-actors expressing the Ekman’s six basic emotions of anger, disgust, fear, happiness, sadness, and surprise, plus neutral tone. This article describes how this database has been created from the recording step to the performed crowdsourcing perception test step. The crowdsourcing has facilitated to statistically validate the emotion of each collected audio sample and also to filter noisy data samples. Hence we obtained two datasets EmoSpanishDB and EmoMatchSpanishDB. The first includes those recorded audios that had consensus during the crowdsourcing process. The second selects from EmoSpanishDB only those audios whose emotion also matches with the originally elicited. Last, we present a baseline comparative study between different state of the art machine learning techniques in terms of accuracy, precision, and recall for both datasets. The results obtained for EmoMatchSpanishDB improves the ones obtained for EmoSpanishDB and thereof, we recommend to follow the methodology that was used for the creation of emotional databases. |
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| AbstractList | In this paper we present a new speech emotion dataset on Spanish. The database is created using an elicited approach and is composed by fifty non-actors expressing the Ekman’s six basic emotions of anger, disgust, fear, happiness, sadness, and surprise, plus neutral tone. This article describes how this database has been created from the recording step to the performed crowdsourcing perception test step. The crowdsourcing has facilitated to statistically validate the emotion of each collected audio sample and also to filter noisy data samples. Hence we obtained two datasets EmoSpanishDB and EmoMatchSpanishDB. The first includes those recorded audios that had consensus during the crowdsourcing process. The second selects from EmoSpanishDB only those audios whose emotion also matches with the originally elicited. Last, we present a baseline comparative study between different state of the art machine learning techniques in terms of accuracy, precision, and recall for both datasets. The results obtained for EmoMatchSpanishDB improves the ones obtained for EmoSpanishDB and thereof, we recommend to follow the methodology that was used for the creation of emotional databases. |
| Author | Salvador, Antonio Barba Pãez, Diego Gachet Garcia-Cuesta, Esteban |
| Author_xml | – sequence: 1 givenname: Esteban surname: Garcia-Cuesta fullname: Garcia-Cuesta, Esteban email: esteban.garcia@fi.upm.es organization: Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid – sequence: 2 givenname: Antonio Barba surname: Salvador fullname: Salvador, Antonio Barba organization: Departamento de Ciencia Y Computación, Universidad Europea de Madrid – sequence: 3 givenname: Diego Gachet surname: Pãez fullname: Pãez, Diego Gachet organization: Departmento de Automãtica, Universidad Francisco de Vitoria |
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| CitedBy_id | crossref_primary_10_3390_s24175797 crossref_primary_10_1016_j_bspc_2025_107636 crossref_primary_10_1109_TASLPRO_2025_3540662 |
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| Keywords | Affective analysis Machine learning Speech emotion recognition EmoMatchSpanishDB Language resources |
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| Snippet | In this paper we present a new speech emotion dataset on Spanish. The database is created using an elicited approach and is composed by fifty non-actors... |
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| SubjectTerms | Audio data Comparative studies Computer Communication Networks Computer Science Crowdsourcing Data Structures and Information Theory Datasets Emotion recognition Emotions Machine learning Multimedia Information Systems Special Purpose and Application-Based Systems Speech recognition State-of-the-art reviews |
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| Title | EmoMatchSpanishDB: study of speech emotion recognition machine learning models in a new Spanish elicited database |
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