Robust speaker identification based on t-distribution mixture model

초록

To minimize the outliers' effects, in this paper, a new speaker identification scheme based on the t-distribution mixture model is proposed. Since the t-distribution provides a longer and heavier tailed alternative to the Gaussian distribution, the mixture model with multivariate t-distribution is expected to show more robust results than the Gaussian mixture model(GMM) in the cases where outliers exist. In experiments, we compared the performance of the proposed scheme with that of using the conventional GMM to show its robustness.

제목
Robust speaker identification based on t-distribution mixture model
저자
Lee, Y; Hahn, H; Han, Y; Lee, J
발행일
2005
학회명
18th Australian Joint Conference on Artificial Intelligence, AI 2005: Advances in Artificial Intelligence
개최지
Sydney
학회 개최일
2005-12-05 ~ 2005-12-09