MODELING OF CONTEXTUAL EFFECTS BASED ON SPECTRAL PEAK INTERACTION

This paper presents a model of contextual effects able to cope with coarticulation problems, especially vowel neutralization. This model is designed to model the superior recognition ability mechanisms of humans and apply these mechanisms to automatic speech recognition and synthesis. It predicts target spectral peaks in reduced vowels, based on interactions between spectral peak pairs. To construct and substantiate the model, psychoacoustic experiments were carried out to measure the extent of phoneme boundary shift with a single formant stimulus as a preceding anchor. The results of the experiments were compared with the spectral peak interaction results obtained from real speech data using the model. This comparison showed that the obtained spectral peak interactions, measured through perceptual boundary shifts with a single formant anchor, are similar to the spectral peak interactions estimated by the model. Additionally, recovery simulations of reduced spectral peak trajectories with real speech data showed that the spectral peak interactions obtained from the psychoacoustic experiments can be used to predict target spectral peaks from reduced spectral peak trajectories in the same manner as the spectral peak interaction function estimated by the model. These results suggest that the model may be emulating aspects of the human mechanisms, that the contextual effects resulting from the interactions between single formant stimuli can play an important role in improving phoneme neutralization recovery, and that the neutralization recovery model can be formulated as the sum of the interactions between spectral peaks. Furthermore, the model can be implemented as a speech recognition preprocessor to reduce recognition error rates because it can overshoot spectral peak trajectories, shift spectral peaks toward their targets, and increase distances among category centers and Bhattacharyya distances between vowel categories.