Application of Speech Processes for the Documentation of Kréyòl Gwadloupéyen (2023)

Abstract: In recent times, there has been a growing number of research studies focused on addressing the challenges posed by low-resource languages and the transcription bottleneck phenomenon. This phenomenon has driven the development of speech recognition methods to transcribe regional and Indigenous languages automatically. Although there is much talk about bridging the gap between speech technologies and field linguistics, there is a lack of documented efficient communication between NLP experts and documentary linguists. The models created for low-resource languages often remain within the confines of computer science departments, while documentary linguistics remain attached to traditional transcription workflows. This paper presents the early stage of a collaboration between NLP experts and field linguists, resulting in the successful transcription of Kréyòl Gwadloupéyen using speech recognition technology.

## introduction the fields of descriptive and documentary linguistics concentrate on gathering information and describing language phenomena. this work is typically performed on small, indigenous, and regional languages that have a limited number of speakers. the linguist's process typically involves recording raw speech, either spontaneous or elicited, transcribing the recordings, translating them, and conducting an analysis. in this pipeline, the transcription becomes the data, but transcribing raw speech is a time-consuming task and is often seen as a bottleneck when a large amount of speech is collected but only a small portion is used. speech technologies have been viewed as a solution to this bottleneck issue by automatically annotating raw speech collections. regular automatic speech recognition (asr) has proven to be challenging due to the lack of data available in most languages to train robust models. however, alternative methods, such as spoken term detection, phone recognition, and the use of universal models, offer new possibilities for collaboration between field linguists and nlp experts. we present here an application of speech processing on raw field linguistics recordings in kréyòl gwadloupéyen. our objective has two parts: firstly, to exhibit the capability of a wav2vec and ctcbased system for our target language, and secondly, to illustrate how the transcription output can be valuable and utilised by field linguists. ## conclusion we have detailed the first stage of a joint effort between field linguists and nlp experts to aid in transcribing kréyòl gwadloupéyen field linguistic data. our approach involved using a voice activity detection system combined with a wav2vec and ctc-based speech recognition model to transcribe raw recordings. the automatically generated transcription was then utilised to establish a gold standard. our initial work has prompted us to consider possibilities beyond conventional metrics such as wer and cer and to explore how even a transcription with a high error rate can still be useful. these early results have led us to question the relevance of standard metrics for evaluating a transcription system that can output words out of vocabulary. while a naive approach would be to assume that an automatically generated transcription is simply a starting point for post-editing and corrections @xcite 2), we have found that it can offer support for creating a gold standard and help transcribers better identify the content of a recording, especially when they are not confident in the target language. moreover, the errors made by the system have increased our understanding of the requirements for a speech recognition system, potentially leading to improved recording quality in the future. moving forward, we will look to improve the output of the system. this will involve utilising an overlapping speech detector to eliminate noisy utterances, employing a language model to prevent token hyper-segmentation, and gradually improving the quality of the training data to enhance the transcription.