Knowledge of at least elementary mathematics is essential for success in many areas of study and a large number of careers. However, many people find this subject difficult, partly due to specialised language and notation. These problems tend to be even more marked for people with disabilities, including blindness or impaired vision, and people with limited motor control or use of their hands.
Learning activities are increasingly carried out in mobile computing environments, replacing the roles of textbooks or handwritten lecture notes. In the near future we expect the modality of use of mobile devices to shift from predominantly output to both input and output. The generally poor performance and usability of text input on these devices motivates the quest for alternative input technologies. One viable alternative input modality is automatic speech recognition, which has matured significantly over the last decade.
We discuss tools that allow the creation and modification of mathematical content using speech, which could be of particular benefit to the groups of disabled people mentioned above. We review previously proposed systems, including our own software for use on desktop PCs, and conclude that there is currently a lack of tools implementing these requirements to a satisfactory level.
We address some technical challenges that need to be solved, including the problem of defining and parsing of suitable languages for spoken mathematics, and focus on the parsing of structured mathematical input, highlighting the importance of incrementality in the process. We believe our incremental parsing algorithm for expressions in operator precedence grammar languages will improve upon previous algorithms for such tasks in terms of performance.