There is no natural way to acquire normalized data so we try to create good enough data to attempt more advanced methods for text normalization. We manually annotated the first normalized corpus in Icelandic, 40,000 sentences, and developed Regína, a rule-based system for text normalization. Regína gets 90.83% accuracy compared to the manually annotated corpus on non-standard words. Regína showed a significant improvement in accuracy when compared to an older normalization system for Icelandic. The normalized corpus and Regína will be released as open source.

We present Talrómur, a large high-quality Text-To-Speech (TTS) corpus for the Icelandic language. This multi-speaker corpus contains recordings from 4 male speakers and 4 female speakers of a wide range in age and speaking style. The corpus consists of 122,417 single utterance recordings equating to approximately 213 hours of voice data. All speakers read from the same script which has a high coverage of possible Icelandic diphones. Manual analysis of 15,956 utterances indicates that the corpus has a reading mistake rate no higher than 0.25%. We additionally present results from subjective evaluations of the different voices with regards to intelligibility, likeability and trustworthiness.

Atli Þór Sigurgeirsson, atlithors@ru.is, Reykjavik University Gunnar Thor Örnólfsson, gunnarthor@hi.is, Árni Magnússon institute of Icelandic studies Dr. Jón Guðnason, jg@ru.is In this paper we present the work of collecting a large amount of high quality speech synthesis data for Icelandic. 8 speakers will be recorded for 20 hours each. A script design strategy is proposed and three scripts have been generated to maximize diphone coverage, varying in length. The largest reading script contains 14,400 prompts and includes 87.3% of all Icelandic diphones at least once and 81% of all Icelandic diphones at least twenty times. A recording client was developed to facilitate recording sessions. The client supports easily importing scripts and maintaining multiple collections in parallel. The recorded data can be downloaded straight from the client. Recording sessions are carried out in a professional studio under supervision and started October of 2019. As of writing, 58.7 hours of high quality speech data has been collected. The scripts, the recording software and the speech data will later be released under a CC-BY 4.0 license.

In this paper, we describe a new national language technology programme for Icelandic. The programme, which spans a period of five years, aims at making Icelandic usable in communication and interactions in the digital world, by developing accessible, open-source language resources and software. The research and development work within the programme is carried out by a consortium of universities, institutions, and private companies, with a strong emphasis on cooperation between academia and industries. Five core projects will be the main content of the programme: language resources, speech recognition, speech synthesis, machine translation, and spell and grammar checking. We also describe other national language technology programmes and give an overview over the history of language technology in Iceland.

This contribution describes an ongoing project of speech data collection, using the web application Samrómur which is built upon Common Voice, Mozilla Foundation’s web platform for open-source voice collection. The goal of the project is to build a large-scale speech corpus for Automatic Speech Recognition (ASR) for Icelandic. Upon completion, Samrómur will be the largest open speech corpus for Icelandic collected from the public domain. We discuss the methods used for the crowd-sourcing effort and show the importance of marketing and good media coverage when launching a crowd-sourcing campaign. Preliminary results exceed our expectations, and in one month we collected data that we had estimated would take three months to obtain. Furthermore, our initial dataset of around 45 thousand utterances has good demographic coverage, is gender-balanced and with proper age distribution. We also report on the task of validating the recordings, which we have not promoted, but have had numerous hours invested by volunteers.