The Role of Articulatory Feature Representation Quality in a Computational Model of Human Spoken-Word Recognition
Odette Scharenborg (TU Delft - Electrical Engineering, Mathematics and Computer Science, Radboud Universiteit Nijmegen)
Danny Merkx (Radboud Universiteit Nijmegen)
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Abstract
Fine-Tracker is a speech-based model of human speech recognition. While previous work has shown that Fine-Tracker is successful at modelling aspects of human spoken-word recognition, its speech recognition performance is not comparable to that of human performance, possibly due to suboptimal intermediate articulatory feature (AF) representations. This study investigates the effect of improved AF representations, obtained using a state-of-the-art deep convolutional network, on Fine-Tracker’s simulation and recognition performance: Although the improved AF quality resulted in improved speech recognition; it, surprisingly, did not lead to an improvement in Fine-Tracker’s simulation power.