Accent Recognition for Noisy Audio Signals
Zichen Ma, Ernest Fokoué · Serdica Journal of Computing · 2015
It is well established that accent recognition can be as accurateas up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminantanalysis, 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 deterioratesfrom 95% to 80%, although the in-sample prediction gives overly-optimistic results.