We adopt automatic language recognition methods to study dialect levelling - a phenomenon that leads to reduced structural differences among dialects in a given spoken language. In terms of dialect characterisation, levelling is a nuisance variable that adversely affects recognition accuracy: The more similar two dialects are, the harder it is to set them apart. We address levelling in Finnish regional dialects using a new SAPU (Satakunta in Speech) corpus containing material from Satakunta (South-Western Finland) between 2007 and 2013. To define a compact and universal set of sound units to characterize dialects, we adopt speech attributes features, namely manner and place of articulation. It will be shown that speech attribute distributions can indeed characterise differences among dialects. Experiments with an i-vector system suggest that (1) the attribute features achieve higher dialect recognition accuracy and (2) they are less sensitive against age-related levelling in comparison to traditional spectral approach.
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