Ill-Posed Problem Feature Extraction Mel-Frequency Cepstral Coefficients Discriminant Analysis Support Vector Machine K-Nearest Neighbors Autoregressive Noise
Issue Date:
2014
Publisher:
Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation:
Serdica Journal of Computing, Vol. 8, No 2, (2014), 169p-182p
Abstract:
It is well established that accent recognition can be as accurate
as up to 95% when the signals are noise-free, using feature extraction
techniques such as mel-frequency cepstral coefficients and binary classifiers such
as discriminant analysis, support vector machine and k-nearest neighbors. In
this paper, we demonstrate that the predictive performance can be reduced
by as much as 15% when the signals are noisy. Specifically, in this paper we
perturb the signals with different levels of white noise, and as the noise
become stronger, the out-of-sample predictive performance deteriorates from
95% to 80%, although the in-sample prediction gives overly-optimistic results. ACM Computing Classification System (1998): C.3, C.5.1, H.1.2, H.2.4., G.3.
