SimPhon.Net

How should a person’s individual differences (IDs, including attention and memory capacity) be built into models of the perception and production of acoustic detail?
How can we model the role attention and memory abilities play in the formation of accurate acoustic-phonetic representations in exemplars or exemplar clouds?
What potential do recurrent neural networks (reservoir computing) have in predicting prosodic parameters in speech production?
How can these networks be evaluated by integrating them e.g. in a speech synthesis platform?

How can exemplar-theoretic models be tested in the laboratory or on large speech corpora and how can specific empirical findings feed back to computational models?
To what extent are episodic representations stored and transferred into long term memory?
When does abstraction / generalization in an exemplar-theoretic model happen?

What level of detail is required in self-oscillating vocal fold models for what phonatory effect — and how can these requirements be evaluated?
How do such models compare to the glottal source models used in parametric speech synthesis?
Can the emergence of prosodic categories be modeled in a production-perception loop using optimization or genetic algorithms?
Is attention modulation a useful concept in modeling the emergence of prosodic (prominence) structure?
Can major prosodic constituents be modeled as emerging from lower level prosodic constituents and morphological boundaries?

How is segmentation of new languages biased by native language knowledge?
What level of abstraction is optimal for exploitation of transition probabilities, e.g., individual phones vs broad classes or syllables?
How are multiple segmentation cues integrated?
How can the above questions be addressed in a machine learning framework: How far does the language of the training material influence segmentation and optimal feature selection?

What can given computational models explain, where do they fail?
How does naïve discriminative learning compare to exemplar theory in modeling speech production and perception?
How can computational models be integrated such that a broader range of phenomena or processes is covered as compared to the coverage of the individual models?
How can the behavior of physiological models inform higher-level neural network models of speech production?
How can models of speech segmentation and prosody be integrated?

Usage-based approaches, computational modeling and simulation studies in phonetics and phonology