ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

Foreign accent detection from spoken Finnish using i-vectors

Hamid Behravan, Ville Hautamäki, Tomi Kinnunen

I-vector based recognition is a well-established technique in stateof- the-art speaker and language recognition but its use in dialect and accent classification has received less attention. We represent an experimental study of i-vector based dialect classification, with a special focus on foreign accent detection from spoken Finnish. Using the CallFriend corpus, we first study how recognition accuracy is affected by the choices of various i-vector system parameters, such as the number of Gaussians, i-vector dimensionality and reduction method. We then apply the same methods on the Finnish national foreign language certificate (FSD) corpus and compare the results to traditional Gaussian mixture model - universal background model (GMM-UBM) recognizer. The results, in terms of equal error rate, indicate that i-vectors outperform GMM-UBM as one expects. We also notice that in foreign accent detection, 7 out of 9 accents were more accurately detected by Gaussian scoring than by cosine scoring.

doi: 10.21437/Interspeech.2013-42

Cite as: Behravan, H., Hautamäki, V., Kinnunen, T. (2013) Foreign accent detection from spoken Finnish using i-vectors. Proc. Interspeech 2013, 79-83, doi: 10.21437/Interspeech.2013-42

  author={Hamid Behravan and Ville Hautamäki and Tomi Kinnunen},
  title={{Foreign accent detection from spoken Finnish using i-vectors}},
  booktitle={Proc. Interspeech 2013},