Nathan Brinklow


Requirements and Motivations of Low-Resource Speech Synthesis for Language Revitalization
Aidan Pine | Dan Wells | Nathan Brinklow | Patrick Littell | Korin Richmond
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.


Natural Language Generation for Polysynthetic Languages: Language Teaching and Learning Software for Kanyen’kéha (Mohawk)
Greg Lessard | Nathan Brinklow | Michael Levison
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

Kanyen’kéha (in English, Mohawk) is an Iroquoian language spoken primarily in Eastern Canada (Ontario, Québec). Classified as endangered, it has only a small number of speakers and very few younger native speakers. Consequently, teachers and courses, teaching materials and software are urgently needed. In the case of software, the polysynthetic nature of Kanyen’kéha means that the number of possible combinations grows exponentially and soon surpasses attempts to capture variant forms by hand. It is in this context that we describe an attempt to produce language teaching materials based on a generative approach. A natural language generation environment (ivi/Vinci) embedded in a web environment (VinciLingua) makes it possible to produce, by rule, variant forms of indefinite complexity. These may be used as models to explore, or as materials to which learners respond. Generated materials may take the form of written text, oral utterances, or images; responses may be typed on a keyboard, gestural (using a mouse) or, to a limited extent, oral. The software also provides complex orthographic, morphological and syntactic analysis of learner productions. We describe the trajectory of development of materials for a suite of four courses on Kanyen’kéha, the first of which will be taught in the fall of 2018.