Speech modelling using cepstral-time feature matrices in hidden Markov models

The paper explores the use of 2-dimensional cepstral-time features for the utilisation of correlation among successive speech spectral vectors, within a hidden-Markov-model (HMM) framework. A cepstral-time-feature matrix is obtained from a 2-dimensional discrete cosine transform of a spectral-time matrix. Advantages of cepstral-time features are that cepstral-time-feature matrices are a simple and robust method of representing short-time variation of speech spectral parameters; a cepstral-time matrix contains information on the transitional dynamics of feature vectors within the matrix; speech recognition based on cepstral time matrices is more robust in noisy environments; and use of a matrix of M cepstral vectors implies a minimum HMM-state duration constraint of M vector units. A simple framework investigated in the paper for applications of cepstral-time features is a finite-state-matrix quantiser (FSMQ), a special case of the HMM. It is used for initialisation of the training phase of HMMs.<>