Foreign accent detection from spoken Finnish using i-vectors
نویسندگان
چکیده
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 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.
منابع مشابه
Factors affecting i-vector based foreign accent recognition: A case study in spoken Finnish
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. In this work, we extensively experiment with the spectral feature based i-vector system on Finnish foreign accent recognition task. Parameters of the system are initially tuned with the CallFriend corpus. Th...
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