A computational model for detecting and perceiving dynamic properities of speech
We investigate many aspects of a computational framework for detecting phonetically salient dynamic properties of the speech signal from continuous speech input. As a prototypical example of this problem, we consider frameworks for the detection of the transition from closure to burst for stop consonants in continuous speech. We formulate the problem as an optimal non‐inear filtering problem. This involves the mapping of the dynamic acoustic data into a low dimension Riemannian manifold in an infinite dimensional Hilbert space. This leads to many kinds of metrics that can now be imposed on the dynamic speech signal and we explore these metrics in the context of the perceptual magnet effect. An extensive discussion of the robustness of the strategy, the nature of the errors made, the relationship to acoustic phonetis and distinctive features and to categorical perception will be conducted. Related problems that have a similar structure include nasal detection using transitions from vowels into adjacent nas...