An Empirical and Computational Investigation of Generalisation in Nonword Reading

Converting text to speech is of interest to researchers in cognitive science, artificial intelligence and technology. Investigations of this process have widely utilized computational modelling and empirical approaches to uncover the mechanisms underlying reading (Coltheart, 2005). As human readers routinely encounter new words, a critical test for a model of reading is whether it can generalize from its training set - i.e. whether it can produce the same kind of pronunciations as humans do for items it has never encountered before. Because of the importance of generalisation, reading nonwords has become a key method in evaluating models of reading.
The vast majority of previous work has focused on modelling reading of monosyllabic words and nonwords. However, excluding multisyllabic words does not provide a representative account of reading English. In an attempt to remedy this situation, Mousikou et al. (2017) compared the performance of current models against human performance in disyllabic nonword pronunciation. In doing so, the authors created the first normative nonword corpus for British English. Comparisons of model and human responses in disyllabic reading led the authors to conclude that these models do not provide a representative account of processes involved in disyllabic reading. Models still produced pronunciations that none of the human participants produced. As such, we are still far from a model capable of truly human-like nonword reading.
One challenge in modelling nonword pronunciation is the high variability in human nonword reading, even among skilled readers. The source of this variability seems to be partly in individual readers and partly in individual items (Coltheart & Ulicheva, 2018). Following spelling-to-sound rules is believed to be the main mechanism applied in nonword reading, but relying on lexical knowledge has also been shown to play a role, at least for some nonwords (Andrews & Scarratt, 1998). The variability in nonword reading may thus be partly based on individual differences in reliance on lexical vs sublexical knowledge, or differences in spelling-to-sound rules and lexical knowledge.
The first aim of the current PhD project is developing a model of reading aloud disyllabic words. To this end, the recently developed corpus of disyllabic nonwords in British English (Mousikou et al. 2017) will be utilized to identify issues in the existing models. Informed by these investigations, a new model with potentially combined features from the existing models will be developed. The second aim of the project is to collect empirical nonword reading data against which to assess the model's generalisation ability, and to explore the variability in human nonword reading. The latter can be explored by comparing the performance of different subgroups of readers in nonword pronunciation. More specifically, the data collection would include two types of responses from the participants: pronunciation of nonwords and ratings of nonword pronunciations given by the newly developed model. Utilizing the richness of the qualitative pronunciation data and the objectivity of the quantitative ratings data in comparisons of human and model performance would maximise the efficiency of these investigations. Analysis of such fine-grained data could also facilitate detection of patterns in nonword reading characterised by particular subgroups of readers.
Better understanding of reading aloud has the potential to contribute to the development of better screening and support for populations with reading difficulties. Additionally, more accurate models of reading aloud can inform the development of assistive technologies, such as speech synthesisers for the visually impaired. The prospect of advancing the development of these applications makes understanding reading aloud a highly valuable endeavour.