A mixture of Gaussians front end for speech recognition

This paper describes a feature extraction technique based on fitting a Gaussian mixture model (GMM) to the speech spectral envelope. The features obtained (the component means, variances and priors) represent both the the general shape of the spectrum and provide information on the position of the spectral peaks. As the features select peaks in the spectrum they are related to the formant amplitudes, locations and bandwidths. Results using the Resource Management corpus, a medium vocabulary task are presented. Although by themselves the GMM features do not outperform MFCC features, systems combining the GMM systems with a standard frontend are shown to give a reduction in word error rate.