Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning
This work addresses the problem of limited annotated data and code-switching for Kurdish speakers, with practical applications in transcription services and multilingual systems, though it is incremental as it adapts existing models.
The study tackled speaker diarization for low-resource languages like Kurdish by fine-tuning Wav2Vec 2.0 on a Kurdish corpus, reducing the diarization error rate by 7.2% and improving cluster purity by 13% compared to a baseline.
Speaker diarization is a fundamental task in speech processing that involves dividing an audio stream by speaker. Although state-of-the-art models have advanced performance in high-resource languages, low-resource languages such as Kurdish pose unique challenges due to limited annotated data, multiple dialects and frequent code-switching. In this study, we address these issues by training the Wav2Vec 2.0 self-supervised learning model on a dedicated Kurdish corpus. By leveraging transfer learning, we adapted multilingual representations learned from other languages to capture the phonetic and acoustic characteristics of Kurdish speech. Relative to a baseline method, our approach reduced the diarization error rate by seven point two percent and improved cluster purity by thirteen percent. These findings demonstrate that enhancements to existing models can significantly improve diarization performance for under-resourced languages. Our work has practical implications for developing transcription services for Kurdish-language media and for speaker segmentation in multilingual call centers, teleconferencing and video-conferencing systems. The results establish a foundation for building effective diarization systems in other understudied languages, contributing to greater equity in speech technology.