The platform samromur.is, or “Samrómur” for short, is a crowdsourcing web application built on Mozilla’s Common Voice, designed to accumulate speech data for the advancement of language technologies in Icelandic. Over the years, Samrómur has proven to be remarkably successful in amassing a significant number of high-quality audio clips from thousands of users. However, the challenge of manually verifying the entirety of the collected data has hindered its effective exploitation, especially in the realm of Automatic Speech Recognition (ASR), its original purpose. In this paper, we introduce the “Samrómur Milljón” corpus, an ASR dataset comprising one million audio clips from Samrómur. These clips have been automatically verified using state-of-the-art speech recognition systems such as NeMo, Wav2Vec2, and Whisper. Additionally, we present the ASR results obtained from creating acoustic models based on Samrómur Milljón. These results demonstrate significant promise when compared to other acoustic models trained with a similar volume of Icelandic data from different sources.

We have developed an open source web reader in Iceland for under-resourced languages. The web reader was developed due to the need for a free and good quality web reader for languages which fall outside the scope of commercially available web readers. It relies on a text-to-speech (TTS) pipeline accessed via a cloud service. The web reader was developed using the Icelandic TTS voices Alfur and Dilja, but could be connected to any language which has a TTS pipeline. The design of our web reader focuses on functionality, adaptability and user friendliness. Therefore, the web reader’s feature set heavily overlaps with the minimal features necessary to provide a good web reading experience while still being extensible enough to be adapted to work for other languages, high-resourced and under-resourced. The web reader works well on all the major web browsers and has a Web Content Accessibility Guidelines 2.0 Level AA: Acceptable compliance, meaning that it works well for the largest user groups, people in under-resourced languages with visual impairments and difficulty reading. The code for our web reader is available and published with an Apache 2.0 license at https://github.com/cadia-lvl/WebRICE, which includes a simple demo of the project.