An adaptive neuro-fuzzy inference system for the qualitative study of perceptual prominence in linguistics

This paper explores the applications of fuzzy logic inference systems as an instrument to perform linguistic analysis in the domain of prosodic prominence. Understanding how acoustic features interact to make a linguistic unit be perceived as more relevant than the surrounding ones is generally needed to study the cognitive processes needed for speech understanding. It also has technological applications in the field of speech recognition and synthesis. We present a first experiment to show how fuzzy inference systems, being characterised by their capability to provide detailed insight about the models obtained through supervised learning can help investigate the complex relationships among acoustic features linked to prominence perception.

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