Modelling Speech Signals Using Formant Frequencies as an Intermediate Representation

Multiple-level segmental hidden Markov models (M-SHMMs) in which the relationship between symbolic and acoustic representations of speech is regulated by a formant-based intermediate representation are considered. New TIMIT phone recognition results are presented, confirming that the theoretical upper-bound on performance is achieved provided that either the intermediate representation or the formant-to-acoustic mapping is sufficiently rich. The way in which M-SHMMs exploit formant-based information is also investigated, using singular value decomposition of the forrnant-to-acoustic mappings and linear discriminant analysis. The analysis shows that if the intermediate layer contains information which is linearly related to the spectral representation, that information is used in preference to explicit formant frequencies, even though the latter are useful for phone discrimination. In summary, although these results confirm the utility of M-SHMMs for automatic speech recognition, they provide empirical evidence of the value of nonlinear formant-to-acoustic mappings.