International audience

Most low resource language technology development is premised on the need to collect data for training statistical models.When we follow the typical process of recording and transcribing text for small Indigenous languages, we hit up against the so-called "transcription bottleneck."Therefore it is worth exploring new ways of engaging with speakers which generate data while avoiding the transcription bottleneck.We have deployed a prototype app for speakers to use for confirming system guesses in an approach to transcription based on word spotting.However, in the process of testing the app we encountered many new problems for engagement with speakers.This paper presents a close-up study of the process of deploying data capture technology on the ground in an Australian Aboriginal community.We reflect on our interactions with participants and draw lessons that apply to anyone seeking to develop methods for language data collection in an Indigenous community.

An increasing number of papers have been addressing issues related to low-resource languages and the transcription bottleneck paradigm. After several years spent in Northern Australia, where some of the strongest Aboriginal languages are spoken, we could observe a gap between the motivations depicted in research contributions in this space and the Northern Australian context. In this paper, we address this gap in research by exploring the potential of speech recognition in an Aboriginal community. We describe our work from training a spoken term detection system to its implementation in an activity with Aboriginal participants. We report here on one side how speech recognition technologies can find their place in an Aboriginal context and, on the other, methodological paths that allowed us to reach better comprehension and engagement from Aboriginal participants.

We propose a novel transcription workflow which combines spoken term detection and humanin-the-loop, together with a pilot experiment.This work is grounded in an almost zero-resource scenario where only a few terms have so far been identified, involving two endangered languages.We show that in the early stages of transcription, when the available data is insufficient to train a robust ASR system, it is possible to take advantage of the transcription of a small number of isolated words in order to bootstrap the transcription of a speech collection.

The CMU Wilderness Multilingual Speech Dataset (Black, 2019) is a newly published multilingual speech dataset based on recorded readings of the New Testament. It provides data to build Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) models for potentially 700 languages. However, the fact that the source content (the Bible) is the same for all the languages is not exploited to date. Therefore, this article proposes to add multilingual links between speech segments in different languages, and shares a large and clean dataset of 8,130 parallel spoken utterances across 8 languages (56 language pairs). We name this corpus MaSS (Multilingual corpus of Sentence-aligned Spoken utterances). The covered languages (Basque, English, Finnish, French, Hungarian, Romanian, Russian and Spanish) allow researches on speech-to-speech alignment as well as on translation for typologically different language pairs. The quality of the final corpus is attested by human evaluation performed on a corpus subset (100 utterances, 8 language pairs). Lastly, we showcase the usefulness of the final product on a bilingual speech retrieval task.