The world is home to an extraordinary level of linguistic diversity, with roughly 7,000 languages currently spoken and signed. Yet this diversity is highly unstable and is being rapidly eroded through a series of complex and interrelated processes that result in or lead to language loss. The combination of monolingualism and networks of global trade languages that are increasingly technologized have led to over half of the world’s population speaking one of only 13 languages. Such linguistic homogenization leaves in its wake a linguistic landscape that is increasingly endangered. A wide range of factors contribute to language loss and attrition. While some—such as natural disasters—are unique to particular language communities and specific geographical regions, many have similar origins and are common across endangered language communities around the globe. The harmful legacy of colonization and the enduring impact of disenfranchising policies relating to Indigenous and minority languages are at the heart of language attrition from New Zealand to Hawai’i, and from Canada to Nepal. Language loss does not occur in isolation, nor is it inevitable or in any way “natural.” The process also has wide-ranging social and economic repercussions for the language communities in question. Language is so heavily intertwined with cultural knowledge and political identity that speech forms often serve as meaningful indicators of a community’s vitality and social well-being. More than ever before, there are vigorous and collaborative efforts underway to reverse the trend of language loss and to reclaim and revitalize endangered languages. Such approaches vary significantly, from making use of digital technologies in order to engage individual and younger learners to community-oriented language nests and immersion programs. Drawing on diverse techniques and communities, the question of measuring the success of language revitalization programs has driven research forward in the areas of statistical assessments of linguistic diversity, endangerment, and vulnerability. Current efforts are re-evaluating the established triad of documentation-conservation-revitalization in favor of more unified, holistic, and community-led approaches.

This paper describes the first, three-year phase of a project at the National Research Council of Canada that creates software to assist Indigenous communities in preserving their languages and extending their use. The project aimed to work within the empowerment paradigm, where collaboration with communities and fulfillment of their goals is central. Since many of the technologies we developed were in response to community needs, the project ended up as a collection of diverse subprojects, including the creation of a sophisticated framework for building verb conjugators for highly inflectional polysynthetic languages (such as Kanyen'kéha, in the Iroquoian language family), release of what is probably the largest available corpus of sentences in a polysynthetic language (Inuktut) aligned with English sentences and experiments with machine translation (MT) systems trained on this corpus, free online services based on automatic speech recognition (ASR) for easing the transcription bottleneck for speech recordings, software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects. Sociolinguistic BackgroundThere are about 70 Indigenous languages from 10 distinct language families currently spoken in Canada (Rice, 2008). Most of these languages have complex morphology; they are polysynthetic or agglutinative. Commonly, a single word carries the meaning of an entire clause in Indo-European languages.

We introduce Waldayu and Waldayu Mobile, web and mobile front-ends for endangered language dictionaries. The Waldayu products are designed with the needs of novice users in mind -both novices in the language and technological novices -and work in tandem with existing lexicographic databases. We discuss some of the unique problems that endangeredlanguage dictionary software products face, and detail the choices we made in addressing them.

This paper describes the motivation and development of speech synthesis systems for the purposes of language revitalization. By building speech synthesis systems for three Indigenous languages spoken in Canada, Kanien'kéha, Gitksan & SENĆOŦEN, we re-evaluate the question of how much data is required to build low-resource speech synthesis systems featuring state-of-the-art neural models. For example, preliminary results with English data show that a FastSpeech2 model trained with 1 hour of training data can produce speech with comparable naturalness to a Tacotron2 model trained with 10 hours of data. Finally, we motivate future research in evaluation and classroom integration in the field of speech synthesis for language revitalization.

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