Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2435

 Title: Accent Recognition for Noisy Audio Signals Authors: Ma, ZichenFokoue, Ernest Keywords: Ill-Posed ProblemFeature ExtractionMel-Frequency Cepstral CoefficientsDiscriminant AnalysisSupport Vector MachineK-Nearest NeighborsAutoregressive 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. URI: http://hdl.handle.net/10525/2435 ISSN: 1312-6555 Appears in Collections: Volume 8 Number 2

Files in This Item:

File Description SizeFormat