Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.

We introduce LinguaMeta, a unified resource for language metadata for thousands of languages, including language codes, names, number of speakers, writing systems, countries, official status, coordinates, and language varieties. The resources are drawn from various existing repositories and supplemented with our own research. Each data point is tagged for its origin, allowing us to easily trace back to and improve existing resources with more up-to-date and complete metadata. The resource is intended for use by researchers and organizations who aim to extend technology to thousands of languages.

This paper presents a methodology for rapidly generating FST-based verbalizers for ASR and TTS systems by efficiently sourcing language-specific data. We describe a questionnaire which collects the necessary data to bootstrap the number grammar induction system and parameterize the verbalizer templates described in Ritchie et al. (2019), and a machine-readable data store which allows the data collected through the questionnaire to be supplemented by additional data from other sources. This system allows us to rapidly scale technologies such as ASR and TTS to more languages, including low-resource languages.