Foreign accent detection from spoken Finnish using i-vectors
Hamid Behravan, Ville Hautamäki, Tomi Kinnunen · 2013
I-vector based recognition is a well-established technique in state-of-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 classi-fication, 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 vari-ous i-vector system parameters, such as the number of Gaus-sians, i-vector dimensionality and reduction method. We then apply the same methods on the Finnish national foreign lan-guage certificate (FSD) corpus and compare the results to tra-ditional 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 ex-pects. We also notice that in foreign accent detection, 7 out of 9 accents were more accurately detected by Gaussian scoring than by cosine scoring. Index Terms: Dialect recognition, foreign accent recognition, i-vector, GMM-UBM, Finnish language