Aidan Pine


2022

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Gi2Pi Rule-based, index-preserving grapheme-to-phoneme transformations
Aidan Pine | Patrick William Littell | Eric Joanis | David Huggins-Daines | Christopher Cox | Fineen Davis | Eddie Antonio Santos | Shankhalika Srikanth | Delasie Torkornoo | Sabrina Yu
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

This paper describes the motivation and implementation details for a rule-based, index-preserving grapheme-to-phoneme engine ‘Gi2Pi' implemented in pure Python and released under the open source MIT license. The engine and interface have been designed to prioritize the developer experience of potential contributors without requiring a high level of programming knowledge. ‘Gi2Pi' already provides mappings for 30 (mostly Indigenous) languages, and the package is accompanied by a web-based interactive development environment, a RESTful API, and extensive documentation to encourage the addition of more mappings in the future. We also present three downstream applications of ‘Gi2Pi' and show results of a preliminary evaluation.

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ReadAlong Studio: Practical Zero-Shot Text-Speech Alignment for Indigenous Language Audiobooks
Patrick Littell | Eric Joanis | Aidan Pine | Marc Tessier | David Huggins Daines | Delasie Torkornoo
Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

While the alignment of audio recordings and text (often termed “forced alignment”) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise “solvable” problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.

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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.

2020

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The Indigenous Languages Technology project at NRC Canada: An empowerment-oriented approach to developing language software
Roland Kuhn | Fineen Davis | Alain Désilets | Eric Joanis | Anna Kazantseva | Rebecca Knowles | Patrick Littell | Delaney Lothian | Aidan Pine | Caroline Running Wolf | Eddie Santos | Darlene Stewart | Gilles Boulianne | Vishwa Gupta | Brian Maracle Owennatékha | Akwiratékha’ Martin | Christopher Cox | Marie-Odile Junker | Olivia Sammons | Delasie Torkornoo | Nathan Thanyehténhas Brinklow | Sara Child | Benoît Farley | David Huggins-Daines | Daisy Rosenblum | Heather Souter
Proceedings of the 28th International Conference on Computational Linguistics

This paper surveys the first, three-year phase of a project at the National Research Council of Canada that is developing software to assist Indigenous communities in Canada 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 recordings of speech in Indigenous languages (and other languages), software for implementing text prediction and read-along audiobooks for Indigenous languages, and several other subprojects.

2018

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Kawennón:nis: the Wordmaker for Kanyen’kéha
Anna Kazantseva | Owennatekha Brian Maracle | Ronkwe’tiyóhstha Josiah Maracle | Aidan Pine
Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages

In this paper we describe preliminary work on Kawennón:nis, a verb conjugator for Kanyen’kéha (Ohsweken dialect). The project is the result of a collaboration between Onkwawenna Kentyohkwa Kanyen’kéha immersion school and the Canadian National Research Council’s Indigenous Language Technology lab. The purpose of Kawennón:nis is to build on the educational successes of the Onkwawenna Kentyohkwa school and develop a tool that assists students in learning how to conjugate verbs in Kanyen’kéha; a skill that is essential to mastering the language. Kawennón:nis is implemented with both web and mobile front-ends that communicate with an application programming interface that in turn communicates with a symbolic language model implemented as a finite state transducer. Eventually, it will serve as a foundation for several other applications for both Kanyen’kéha and other Iroquoian languages.

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Indigenous language technologies in Canada: Assessment, challenges, and successes
Patrick Littell | Anna Kazantseva | Roland Kuhn | Aidan Pine | Antti Arppe | Christopher Cox | Marie-Odile Junker
Proceedings of the 27th International Conference on Computational Linguistics

In this article, we discuss which text, speech, and image technologies have been developed, and would be feasible to develop, for the approximately 60 Indigenous languages spoken in Canada. In particular, we concentrate on technologies that may be feasible to develop for most or all of these languages, not just those that may be feasible for the few most-resourced of these. We assess past achievements and consider future horizons for Indigenous language transliteration, text prediction, spell-checking, approximate search, machine translation, speech recognition, speaker diarization, speech synthesis, optical character recognition, and computer-aided language learning.

2017

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Waldayu and Waldayu Mobile: Modern digital dictionary interfaces for endangered languages
Patrick Littell | Aidan Pine | Henry Davis
Proceedings of the 2nd Workshop on the Use of Computational Methods in the Study of Endangered Languages