Hidden spectral peak trajectory model for phone classification

It is well known that spectrogram readers can classify different phones from their spectral-time characteristics, such as the formants. In this paper we present a novel acoustic model for phone classification based on the implicit estimation of the spectral peak trajectory as a polynomial time function. By making use of the known relationship between the spectral peak information and the cepstral coefficients, cepstral-based phone trajectories are built as functions of the hidden spectral trajectories. This captures the intuitive formant trajectories in the spectral domain while allowing speech modeling to be done in the more familiar cepstral domain. We have evaluated this hidden spectral peak trajectory model in both vowel classification and phone classification tasks. On a simple single Gaussian model, the hidden spectral peak trajectory model outperforms the HMM on both vowel and phone classification tasks. The new method can also be combined with the HMM model. This combination performs better than a more complex HMM with similar number of parameters.