CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models

Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models. This could present a significant obstacle for language community members and linguists to use NLP tools. This paper introduces the CMU Linguistic Annotation Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages, even with limited training data. We describe various tools and APIs that are currently available and how developers can easily add new models/functionality to the framework. Code is available at https://github.com/neulab/cmulab along with a live demo at https://cmulab.devComment: Live demo at https://cmulab.de

Automatic Speech Recognition for Supporting Endangered Language Documentation

Generating accurate word-level transcripts of recorded speech for language documentation is difficult and time-consuming, even for skilled speakers of the target language. Automatic speech recognition (ASR) has the potential to streamline transcription efforts for endangered language documentation, but the practical utility of ASR for this purpose has not been fully explored. In this paper, we present results of a study in which both linguists and community members, with varying levels of language proficiency, transcribe audio recordings of an endangered language under timed conditions with and without the assistance of ASR. We find that both time-to-transcribe and transcription error rates are significantly reduced when correcting ASR for language learners of all levels. Despite these improvements, most community members in our study express a preference for unassisted transcription, highlighting the need for developers to directly engage with stakeholders when designing and deploying technologies for supporting language documentation.National Foreign Language Resource Cente