Accent Recognition for Noisy Audio Signals
Keywords:Ill-Posed Problem, Feature Extraction, Mel-Frequency Cepstral Coefficients, Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, Autoregressive Noise
AbstractIt 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.