Domain Adaptation of the Pyannote Diarization Pipeline for Conversational Indonesian Audio
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866909983325552640 |
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| author | Prasetyo, Muhammad Daffa'i Rafi Putra, Ramadhan Andika Ilmi, Zaidan Naufal Azizah, Kurniawati |
| author_facet | Prasetyo, Muhammad Daffa'i Rafi Putra, Ramadhan Andika Ilmi, Zaidan Naufal Azizah, Kurniawati |
| contents | This study presents a domain adaptation approach for speaker diarization targeting conversational Indonesian audio. We address the challenge of adapting an English-centric diarization pipeline to a low-resource language by employing synthetic data generation using neural Text-to-Speech technology. Experiments were conducted with varying training configurations, a small dataset (171 samples) and a large dataset containing 25 hours of synthetic speech. Results demonstrate that the baseline \texttt{pyannote/segmentation-3.0} model, trained on the AMI Corpus, achieves a Diarization Error Rate (DER) of 53.47\% when applied zero-shot to Indonesian. Domain adaptation significantly improves performance, with the small dataset models reducing DER to 34.31\% (1 epoch) and 34.81\% (2 epochs). The model trained on the 25-hour dataset achieves the best performance with a DER of 29.24\%, representing a 13.68\% absolute improvement over the baseline while maintaining 99.06\% Recall and 87.14\% F1-Score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03684 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Domain Adaptation of the Pyannote Diarization Pipeline for Conversational Indonesian Audio Prasetyo, Muhammad Daffa'i Rafi Putra, Ramadhan Andika Ilmi, Zaidan Naufal Azizah, Kurniawati Sound I.2.7; H.5.1 This study presents a domain adaptation approach for speaker diarization targeting conversational Indonesian audio. We address the challenge of adapting an English-centric diarization pipeline to a low-resource language by employing synthetic data generation using neural Text-to-Speech technology. Experiments were conducted with varying training configurations, a small dataset (171 samples) and a large dataset containing 25 hours of synthetic speech. Results demonstrate that the baseline \texttt{pyannote/segmentation-3.0} model, trained on the AMI Corpus, achieves a Diarization Error Rate (DER) of 53.47\% when applied zero-shot to Indonesian. Domain adaptation significantly improves performance, with the small dataset models reducing DER to 34.31\% (1 epoch) and 34.81\% (2 epochs). The model trained on the 25-hour dataset achieves the best performance with a DER of 29.24\%, representing a 13.68\% absolute improvement over the baseline while maintaining 99.06\% Recall and 87.14\% F1-Score. |
| title | Domain Adaptation of the Pyannote Diarization Pipeline for Conversational Indonesian Audio |
| topic | Sound I.2.7; H.5.1 |
| url | https://arxiv.org/abs/2601.03684 |