  The objective of this work is to investigate complementary features which can
aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task
of closed, limited set word recognition for non-native English speakers of
different mother-tongues. Unlike the MFCCs, which are derived from the spectral
energy of the speech signal, the proposed frequency-centroids (FCs) encapsulate
the spectral centres of the different bands of the speech spectrum, with the
bands defined by the Mel filterbank. These features, in combination with the
MFCCs, are observed to provide relative performance improvement in English word
recognition, particularly under varied noisy conditions. A two-stage
Convolution Neural Network (CNN) is used to model the features of the English
words uttered with Arabic, French and Spanish accents.
