Spell-checking and correction is one of the key applications of natural language support. Historically, for the biggest, less morphologically complex languages, spell-checking and correction could be implemented by relatively simple means; however, for morphologically complex and low-resource languages, the solutions were often suboptimal. Finite-state methods are the state of the art in rule-based natural language processing and also for spell-checking and correction they have been effectively used. In this article, we show some recent developments of a finite-state spell-checker implementation that works with modern operating systems and platforms.

Creating language technology based on language data has become very popular with the recent advances of large language models and neural network technologies. This makes language resources very valuable, and especially in case of indigenous languages, the scarce resources are even more precious. Given the good results of simply fetching everything you can from the internet and feeding it to neural networks in English, there has been more work on doing the same for all languages. However, indigenous language resources as they are on the web are not comparable in that they would encode the most recent normativised language in all its aspects. This problematic is further due to not understanding the texts input to models or output by models by the people who work on them. Corpora also have intelligent property rights and copyrights that are not respected. Furthermore, the web is filled with the result of language model -generated texts. In this article we describe an ethical and sustainable way to work with indigenous languages.

Long term language technology infrastructures are critical for continued maintenance of language technology based software that is used to support the use of languages in digital world. In Nordic area we have languages ranging from well-resourced national majority languages like Norwegian, Swedish and Finnish as well as minoritised, unresourced and indigenous languages like Sámi languages. We present an infrastructure that has been build in over 20 years time that supports building language technology and tools for most of the Nordic languages as well as many of the languages all over the world, with focus on Sámi and other indigenous, minoritised and unresourced languages. We show that one common infrastructure can be used to build tools from keyboards and spell-checkers to machine translators, grammar checkers and text-to-speech as well as automatic speech recognition.

Machine learning (ML) approaches have dominated NLP during the last two decades. From machine translation and speech technology, ML tools are now also in use for spellchecking and grammar checking, with a blurry distinction between the two. We unmask the myth of effortless big data by illuminating the efforts and time that lay behind building a multi-purpose corpus with regard to collecting, mark-up and building from scratch. We also discuss what kind of language technology minority languages actually need, and to what extent the dominating paradigm has been able to deliver these tools. In this context we present our alternative to corpus-based language technology, which is knowledge-based language technology, and we show how this approach can provide language technology solutions for languages being outside the reach of machine learning procedures. We present a stable and mature infrastructure (GiellaLT) containing more than hundred languages and building a number of language technology tools that are useful for language communities.

This paper presents a work-in-progress report of an open-source speech technology project for indigenous Sami languages. A less detailed description of this work has been presented in a more general paper about the whole GiellaLT language infrastructure, submitted to the LREC 2022 main conference. At this stage, we have designed and collected a text corpus specifically for developing speech technology applications, namely Text-to-speech (TTS) and Automatic speech recognition (ASR) for the Lule and North Sami languages. We have also piloted and experimented with different speech synthesis technologies using a miniature speech corpus as well as developed tools for effective processing of large spoken corpora. Additionally, we discuss effective and mindful use of the speech corpus and also possibilities to use found/archive materials for training an ASR model for these languages.