A Database of Age and Gender Annotated Telephone Speech
By Felix Burkhardt, Martin Eckert, Wiebke Johannsen, J. Stegmann. 2010
97 citations recorded by the source corpus.
This article describes an age-annotated database of German telephone speech. All in all 47 hours of prompted and free text was recorded, uttered by 954 paid participants in a style typical for automated voice services. The participants were selected based on an equal distribution of males and females within four age cluster groups; children, youth, adults and seniors. Within the children, gender is not distinguished, because it doesnt have a strong enough effect on the voice. The textual content was designed to be typical for automated voice services and consists mainly of short commands, single words and numbers. An additional database consists of 659 speakers (368 female and 291 male) that called an automated voice portal server and answered freely on one of the two questions What is your favourite dish? and What would you take to an island? (island set, 422 speakers). This data might be used for out-of domain testing. The data will be used to tune an age-detecting automated voice service and might be released to research institutes under controlled conditions as part of an open age and gender detection challenge.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Björn Schuller
- Anton Batliner
- Stefan Steidl
- Laurence Y. Devillers
- Shrikanth S. Narayanan
- Felix Burkhardt
- Christian A. Müller
- Yang Trista Cao
Citations in the corpus
- Paralinguistics in speech and language - State-of-the-art and the challenge (2013, methodology): “In the Age and Gender Sub-Challenge, the ‘aGender’ corpus was used for analyses and comparison (Burkhardt et al., 2010).”
- The INTERSPEECH 2010 paralinguistic challenge (2010, background): “Each of the six recording sessions contained 18 utterances taken from a set of utterances listed in detail in [1].”
- Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle* (2021, background): “Ng (2010) Y Y Y Y N Y Y N Burkhardt et al. (2010) N N Y N − Y Y − Marton, Habash, and Rambow (2010) N Y N N − − − − Le Nagard and Koehn (2010) Y Y Y Y N Y Y N Rojas-Barahona et al.”