Wav2Gloss: Generating Interlinear Glossed Text from Speech (2403.13169v2)

Abstract: Thousands of the world's languages are in danger of extinction--a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages' communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches.

• The paper introduces wav2gloss, automating interlinear glossed text extraction from speech using a novel 37-language Fieldwork dataset.
• It compares end-to-end models with cascaded ASR-to-gloss pipelines, highlighting the benefits of direct sequence-to-sequence approaches.
• Findings reveal that end-to-end systems reduce error propagation and leverage pre-trained lexical cues to enhance IGT annotation quality.

Generating Interlinear Glossed Text from Speech with wav2gloss

Introduction to wav2gloss

The documentation of endangered languages represents a critical endeavor in the preservation of cultural diversity. Interlinear Glossed Text (IGT), a fundamental tool in linguistic research, facilitates the analysis of unfamiliar languages by providing a word-by-word or morpheme-by-morpheme translation. The wav2gloss task, as proposed, seeks to automate the extraction of IGT annotations from speech recordings—a process traditionally marked by intensive manual labor. This post explores the intricacies of wav2gloss, emphasizing the challenges addressed, methodologies employed, and the implications of the associated paper.

The Fieldwork Dataset

At the core of this research is the introduction of the Fieldwork dataset, designed specifically for the wav2gloss task. Compiling speech data from 37 languages, Fieldwork stands as the first dataset to merge speech recordings with comprehensive IGT annotations, including transcriptions, morphological segmentation, glosses, and translations. The data derivation process involved meticulous selection, standardization, and partitioning strategies to prepare the dataset for machine learning applications. Such efforts underscore the complexity of working with linguistic field data and spotlight the meticulous attention to detail required in dataset construction.

Methodological Overview

The paper compares two primary approaches to the wav2gloss task: end-to-end models and cascaded systems. Leveraging pre-trained speech models (WavLM, XLS-R, and OWSM) modified for sequence-to-sequence tasks, the end-to-end approach directly predicts IGT annotations from speech. On the other hand, the cascaded system first transcribes speech into text using an ASR model, then employs a text-to-gloss model for further annotation. This bifurcation in methodology allows for a comprehensive evaluation of the task's challenges, highlighting the pivotal role of model choice and training strategy in achieving effective IGT generation.

Analytical Insights

The comparison between end-to-end and cascaded systems reveals nuanced performance disparities across various subtasks of IGT annotation. Notably, end-to-end systems exhibit superior performance in scenarios where pre-trained decoders assist with translation and glossing, specifically benefiting from the lexical knowledge embedded in such models. Conversely, the cascaded approach, despite its anticipated advantage in leveraging text-based annotation models, struggles with error propagation, an issue less pronounced in end-to-end systems. Additionally, the analysis uncovers the limitations of multi-task learning in this context, suggesting a potential interference between diverse annotation tasks when modeled simultaneously.

Future Directions and Theoretical Implications

The wav2gloss task opens new horizons for research in language documentation and computational linguistics. The paper's findings emphasize the need for developing machine learning models capable of handling the complexities inherent to linguistic annotation from speech. Future work may explore more sophisticated model architectures, novel pre-training strategies, or multimodal approaches combining speech and text inputs. Theoretically, this research advances our understanding of multilingual model adaptation and the challenges of transferring knowledge across languages with limited resources.

Conclusion

The wav2gloss task represents a pioneering step toward automating the generation of IGT from speech, a development with profound implications for the documentation and preservation of endangered languages. Through the Fieldwork dataset and benchmark models, this research lays the groundwork for future explorations in this domain, challenging the computational linguistics community to devise innovative solutions to the intricate problem of annotating linguistic field data. As this endeavor progresses, it holds the promise of significantly enhancing the efficiency of language documentation practices, thereby contributing to the broader objectives of linguistic diversity and cultural heritage preservation.