Recognition of voiced speech from the bispectrum

Abstract

Recognition of voiced speech phonemes is addressed in this paper using features extracted from the bispectrum of the speech signal. Voiced speech is modeled as a superposition of coupled harmonics, located at frequencies that are multiples of the pitch and modulated by the vocal tract. For this type of signal, nonzero bispectral values are shown to be guaranteed by the estimation procedure employed. The vocal tract frequency response is reconstructed from the bispectrum on a set of frequency points that are multiples of the pitch. An AR model is next fitted on this transfer function. The AR coefficients are used as the feature vector for the subsequent classification step. Any finite dimension vector classifier can be employed at this point. Experiments using the LVQ neural classifier give satisfactory classification scores on real speech data, extracted from the DARPA/TIMIT speech corpus.