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Journals / Zeki Sistemler Teori ve Uygulamaları Dergisi / 2025 / Cilt: 8 Sayı: 2

Enhancing Speaker Diarization Accuracy of PyAnnote by Fine-tuning

İnce Ayar ile PyAnnote Kullanılarak Konuşmacı Güncesi Çıkarımı Doğruluğunun Artırılması

Amine Gonca Toprak(AdresGezgini A.Ş.)0000-0003-2425-5342
Sercan Çepni(AdresGezgini A.Ş.)0000-0002-3405-6059
Şükrü Ozan(digiMOST GmbH)0000-0002-3227-348X
Pages
105–114
Publication Date
2025-09-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

After fine-tuning, the DER score of PyAnnote 3.1 decreased from 21.9% to 15.6%.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Speech processing plays a crucial role in various applications such as speaker diarization, speech recognition, and sound event detection.
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Key Findings & Data:PyAnnote models were fine-tuned using a dataset obtained from the annotation of company call center recordings and evaluated against the baseline model performances.
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Conclusion & Impact:After fine-tuning, the DER score of PyAnnote 3.1 decreased from 21.9% to 15.6%.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:konuşmacı güncesi çıkarımı · pyannote · ince ayar

Abstract

Speech processing plays a crucial role in various applications such as speaker diarization, speech recognition, and sound event detection. This study utilizes PyAnnote, a powerful open-source speaker diarization tool, to conduct a comparative analysis on company call center recordings. PyAnnote models were fine-tuned using a dataset obtained from the annotation of company call center recordings and evaluated against the baseline model performances. Model performances were assessed using the DER metric, and as a result, the fine-tuned PyAnnote 3.1 model demonstrated superior performance. After fine-tuning, the DER score of PyAnnote 3.1 decreased from 21.9% to 15.6%.

Özet

Konuşma işleme, konuşmacı güncesi çıkarımı, konuşma tanıma ve ses olayı tespiti gibi birçok uygulamada önemli bir rol oynamaktadır. Bu çalışma, firma çağrı merkezi kayıtları üzerinde karşılaştırmalı bir analiz sunmak amacıyla PyAnnote adlı güçlü, açık kaynaklı konuşmacı güncesi çıkarım aracını kullanmaktadır. PyAnnote modelleri, firma çağrı merkezi kayıtlarının etiketlenmesiyle elde edilen veri seti ile ince ayar yapılarak değerlendirilmiş ve baz model performansları ile karşılaştırılmıştır. Model performansları, DER metriği kullanılarak değerlendirilmiş ve sonuç olarak, ince ayar yapılan PyAnnote 3.1 modeli üstün performans göstermiştir. İnce ayar sonrası PyAnnote 3.1 versiyonunun DER puanı %21.9’dan %15.6’ya düşmüştür.

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Cite This Article

Toprak, A. G., Çepni, S., Ozan, Ş. (2025). Enhancing Speaker Diarization Accuracy of PyAnnote by Fine-tuning. Zeki Sistemler Teori ve Uygulamaları Dergisi, Cilt: 8 Sayı: 2, 105–114. https://doi.org/10.38016/jista.1563512

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