Automatic Transcription of Grammaticality Judgements for Language Documentation (2024)

Abstract: Descriptive linguistics is a sub-field of linguistics that involves the collection and annotationof language resources to describe linguistic phenomena. The transcription of these resources is often described as a tedious task, and Automatic Speech Recognition (ASR) has frequently been employed to support this process. However, the typical research approach to ASR in documentary linguistics often only captures a subset of the field’s diverse reality. In this paper, we focus specifically on one type of data known as grammaticality judgment elicitation in the context of documenting Kréyòl Gwadloupéyen. We show that only a few minutes of speech is enough to fine-tune a model originally trained in French to transcribe segments in Kréyol.

## introduction under-resourced languages, characterized by insufficient data to train common statistical or neural models, stand in contrast to high-resource languages like english, french, and mandarin. the egids scale @xcite offers a more nuanced classification, assessing endangerment based on socio-political factors such as the number of speakers or support from public institutions. this scale can be used to assess a language's resource level and its ability to acquire linguistic resources. for instance, the presence of media representation in a language suggests a wealth of transcribed speech, while languages lacking media exposure or educational institutions typically possess limited data, often from descriptive linguistics efforts. in the context of under-resourced languages falling below level 4 on the egids scale, where a comprehensive educational system is lacking, attention has been directed towards advancing speech technologies. these innovations aim to assist linguists in overcoming the transcription bottleneck, thereby expediting the creation of new transcribed spoken resources. one potential procedural approach involves collecting a few hours of transcribed monolingual speech in the target language, training a model with this data, and subsequently employing the model to automatically transcribe new recordings. despite the demonstrated effectiveness of such a workflow @xcite , it's crucial to acknowledge that monolingual data capture only a subset of the diverse recordings compiled by linguists in the field. grammaticality judgments constitute a form of interview commonly carried out in a shared language of the linguist and the speaker, involving one or more linguists engaging with one or more speakers to discuss grammatical structures in the target language. this dynamic interaction is inherently multilingual, featuring spontaneous speech from various contributors. in the context of the documentation of kréyol gwadeloupéyen (iso-gcf), this paper aims to investigate the efficacy of cuttingedge speech recognition architectures in transcribing such recordings, even when confronted with severely limited available data. ## conclusions here, we present the initial outcomes of our efforts to fine-tune a large speech recognition model for code-switching between two closely related languages: kréyòl gwadloupéyen and its lexifier, french. our focus is the specific scenario of grammaticality judgments, where linguists engage in conversations with native speakers in their shared language to evaluate the correctness of specific sentences in the target language. the preliminary results illustrate that fine-tuning the whisper model using just 20 minutes of speech substantially improves transcription quality. the refined model demonstrates increased resilience in transcribing noisy fieldwork data and accurately transcribes approximately 60% of the codeswitched segments in our test set. this enhancement facilitates direct access to queries in the target language, which were consistently misinterpreted by the french model. we present the findings of our initial experiments here, with more comprehensive details to be provided in future work. subsequent experiments will involve comparing the whisper model with alternative architectures, such as wav2vec, and expanding the scope of experiments to encompass grammaticality judgments in other languages.