Speech Generation for Indigenous Language Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the quality of contemporary speech synthesis improves, so too does the interest from language communities in developing text-to-speech (TTS) systems for a variety of real-world applications. Much of the work on TTS has focused on high-resource languages, resulting in implicitly resource-intensive paths to building such systems. The goal of this paper is to provide signposts and points of reference for future low-resource speech synthesis efforts, with insights drawn from the Speech Generation for Indigenous Language Education (SGILE) project. Funded and coordinated by the National Research Council of Canada (NRC), this multi-year, multi-partner project has the goal of producing high-quality text-to-speech systems that support the teaching of Indigenous languages in a variety of educational contexts. We provide background information and motivation for the project, as well as details about our approach and project structure, including results from a multi-day requirements-gathering session. We discuss some of our key challenges, including building models with appropriate controls for educators, improving model data efficiency, and strategies for low-resource transfer learning and evaluation. Finally, we provide a detailed survey of existing speech synthesis software and introduce EveryVoice TTS, a toolkit designed specifically for low-resource speech synthesis. • We provide background and points of reference for future low-resource TTS projects • We describe four main technical challenges for low-resource speech synthesis • We introduce the EveryVoice TTS Toolkit designed specifically for low-resource TTS • We compare the EveryVoice TTS Toolkit with six other existing toolkits
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it