BIAS in Flemish automatic speech recognition
Aaricia Herygers (Technische Hochschule Ingolstadt)
Vass Verkhodanova (Rijksuniversiteit Groningen)
Matt Coler (Rijksuniversiteit Groningen)
Odette Scharenborg (TU Delft - Multimedia Computing)
Munir Georges (Intel Corporation, Technische Hochschule Ingolstadt)
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Abstract
Research has shown that automatic speech recognition (ASR) systems exhibit biases against different speaker groups, e.g., based on age or gender. This paper presents an investigation into bias in recent Flemish ASR. Seeing as Belgian Dutch, which is also known as Flemish, is often not included in Dutch ASR systems, a state-of-the-art ASR system for Dutch is trained using the Netherlandic Dutch data from the Spoken Dutch Corpus. Using the Flemish data from the JASMIN-CGN corpus, word error rates for various regional variants of Flemish are then compared. In addition, the most misrecognized phonemes are compared across speaker groups. The evaluation confirms a bias against speakers from West Flanders and Limburg, as well as against children, male speakers, and non-native speakers.