Accent Recognition for Noisy Audio Signals
Data(s) |
03/04/2015
03/04/2015
2014
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Resumo |
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. |
Identificador |
Serdica Journal of Computing, Vol. 8, No 2, (2014), 169p-182p 1312-6555 |
Idioma(s) |
en |
Publicador |
Institute of Mathematics and Informatics Bulgarian Academy of Sciences |
Palavras-Chave | #Ill-Posed Problem #Feature Extraction #Mel-Frequency Cepstral Coefficients #Discriminant Analysis #Support Vector Machine #K-Nearest Neighbors #Autoregressive Noise |
Tipo |
Article |