A data-driven model of acoustic speech intelligibility for optimization-based models of speech production

This paper presents a data-driven model of intelligibility which is intended to be used in an optimization-based model of speech production.The BiLSTM-based model is trained as a phoneme classifier and takes a sequence of real articulatory trajectories as input and returns the probability of phonemes over time.The optimization minimizes a cost function which is the weighted sum of the conflicting demands of being intelligible and least articulatory effort.The data-driven intelligibility model presented in this paper is used to compute the intelligibility score.Simulations support Lindblom's hypo-and hyper-articulation theory of speech, as the degree of hyper-articulation of speech can be modified and tuned along a continuum by balancing the importance given to both requirements of intelligibility and least articulatory effort.