Modifications of KNN classifier for speaker identification system
Juraj Kačur · 2016
In this article modifications and adjustments of weighted K-nearest neighbor (KNN) classification method are discussed. The main focus is on KNN performance in the speaker text independent identification task, operating in real time and minimizing the enrolment time for a new user. The main concern was in design of weighting schemes for feature distance and application of different data dependent supervised and unsupervised feature transformations applied either locally or globally. All tests were accomplished on a speaker database containing 2 environments, having training and testing parts. The results were compared to a standard Gaussian Mixture Model (GMM) method. It was shown that the best results do not significantly change with the used weighting schemes if they are properly tuned for certain environment. However, Gaussian window is better in terms of fine tuning and in average accuracy. Further, KNN outperformed GMM when no universal background model (UBM) was used, and comparable results were achieved if MAP adaptation from UMB was applied. For 21 speakers the best averaged accuracy was over 94% measured on 3s intervals.