A Comparative Analysis of Traditional and Deep Learning Approaches for Adressing Challenges in Speaker Diarization

Emsal Altinay; Ecir Uğur Küçüksille

Makale detayları

A Comparative Analysis of Traditional and Deep Learning Approaches for Adressing Challenges in Speaker Diarization

Yazarlar:

Emsal Altinay | Ecir Uğur Küçüksille

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 5, pp.1095-1105

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358476

pISSN:

2149-3367

Anahtar kelimeler:

Özet:

 Speaker diarization is the task of distinguishing and segmenting speech from multiple speakers in an audio recording, a crucial task for various applications such as meeting transcription, voice activated systems, and audio indexing. Traditional clustering-based methods have been widely used, but they struggle with challenges in real-world scenarios, including noisy environments, overlapping speech, speaker variability and variable recording conditions. This study addresses these limitations by focusing on deep learning-based approaches, which have demonstrated significant advancements in improving the accuracy of multi-speaker diarization. The aim of this study is to compare traditional clustering methods with new deep learning techniques, including Time Delay Neural Networks TDNN, End-to-End Neural Diarization EEND, and the Fully Supervised UIS-RNN, to solve the challenges of multi-speaker diarization. The results show that on the CallHome dataset, TDNN systems indicated slight improvements in non-overlapping speech, with a Diarization Error Rate DER of 12-14, in comparison to 13-15 for traditional clustering methods. However, in overlapping speech, EEND outperformed traditional methods, achieving a DER of 12.6, which was significantly lower than the 23.7 observed with traditonal clustering. The Fully Supervised UIS-RNN model delivered the best overall performance, achieving a DER of 7.6. Future research should focus on integrating the strengths of traditional and deep learning techniques while reducing the computational and data requirements for more accessible, real-time speaker diarization systems. The findings indicated that deep learning will make a substantial contribution to the field of speaker diarisation.

 Speaker diarization is the task of distinguishing and segmenting speech from multiple speakers in an audio recording, a crucial task for various applications such as meeting transcription, voice activated systems, and audio indexing. Traditional clustering-based methods have been widely used, but they struggle with challenges in real-world scenarios, including noisy environments, overlapping speech, speaker variability and variable recording conditions. This study addresses these limitations by focusing on deep learning-based approaches, which have demonstrated significant advancements in improving the accuracy of multi-speaker diarization. The aim of this study is to compare traditional clustering methods with new deep learning techniques, including Time Delay Neural Networks TDNN, End-to-End Neural Diarization EEND, and the Fully Supervised UIS-RNN, to solve the challenges of multi-speaker diarization. The results show that on the CallHome dataset, TDNN systems indicated slight improvements in non-overlapping speech, with a Diarization Error Rate DER of 12-14, in comparison to 13-15 for traditional clustering methods. However, in overlapping speech, EEND outperformed traditional methods, achieving a DER of 12.6, which was significantly lower than the 23.7 observed with traditonal clustering. The Fully Supervised UIS-RNN model delivered the best overall performance, achieving a DER of 7.6. Future research should focus on integrating the strengths of traditional and deep learning techniques while reducing the computational and data requirements for more accessible, real-time speaker diarization systems. The findings indicated that deep learning will make a substantial contribution to the field of speaker diarisation.

 Speaker diarization is the task of distinguishing and segmenting speech from multiple speakers in an audio recording, a crucial task for various applications such as meeting transcription, voice activated systems, and audio indexing. Traditional clustering-based methods have been widely used, but they struggle with challenges in real-world scenarios, including noisy environments, overlapping speech, speaker variability and variable recording conditions. This study addresses these limitations by focusing on deep learning-based approaches, which have demonstrated significant advancements in improving the accuracy of multi-speaker diarization. The aim of this study is to compare traditional clustering methods with new deep learning techniques, including Time Delay Neural Networks TDNN, End-to-End Neural Diarization EEND, and the Fully Supervised UIS-RNN, to solve the challenges of multi-speaker diarization. The results show that on the CallHome dataset, TDNN systems indicated slight improvements in non-overlapping speech, with a Diarization Error Rate DER of 12-14, in comparison to 13-15 for traditional clustering methods. However, in overlapping speech, EEND outperformed traditional methods, achieving a DER of 12.6, which was significantly lower than the 23.7 observed with traditonal clustering. The Fully Supervised UIS-RNN model delivered the best overall performance, achieving a DER of 7.6. Future research should focus on integrating the strengths of traditional and deep learning techniques while reducing the computational and data requirements for more accessible, real-time speaker diarization systems. The findings indicated that deep learning will make a substantial contribution to the field of speaker diarisation.

 Konuşmacı diyarizasyonu, bir ses kaydında birden fazla konuşmacıdan gelen konuşmayı ayırt etme ve bölümlere ayırma görevidir ve toplantı transkripsiyonu, sesle etkinleştirilen sistemler ve ses indeksleme gibi çeşitli uygulamalar için çok önemli bir görevdir. Geleneksel kümeleme tabanlı yöntemler yaygın olarak kullanılmaktadır, ancak gürültülü ortamlar, örtüşen konuşma, konuşmacı değişkenliği ve değişken kayıt koşulları gibi gerçek dünya senaryolarındaki zorluklarla mücadele etmektedirler. Bu çalışma, çok hoparlörlü diyarizasyonunun doğruluğunu artırmada önemli gelişmeler gösteren derin öğrenme tabanlı yaklaşımlara odaklanarak bu sınırlamaları ele almaktadır. Bu makalenin amacı, geleneksel kümeleme yöntemlerini Zaman Gecikmeli Sinir Ağları TDNN, Uçtan Uca Sinirsel Günlük Oluşturma EEND ve Tam Denetimli UIS-RNN gibi derin öğrenme teknikleriyle karşılaştırarak çok konuşmacılı diyarizasyon zorluklarını çözmektir. Sonuçlar, CallHome veri setinde, TDNN sistemlerinin, geleneksel kümeleme yöntemleri için 13-15e kıyasla, 12-14lük bir Diyarizasyon Hata Oranı DER ile örtüşmeyen konuşmada hafif iyileşmeler gösterdiğini göstermektedir. Bununla birlikte, örtüşen konuşmada, EEND geleneksel yöntemlerden daha iyi performans göstermiş ve geleneksel kümeleme ile gözlemlenen 23,7den önemli ölçüde daha düşük olan 12,6lık bir DER elde etmiştir. Tam Denetimli UIS-RNN modeli, 7,6lık bir DER elde ederek en iyi genel performansı sağlamıştır. Bulgular, derin öğrenmenin konuşmacı diyarizasyonu alanında önemli bir katkı sağlayacağını göstermiştir.

Makale detayları

A Comparative Analysis of Traditional and Deep Learning Approaches for Adressing Challenges in Speaker Diarization

Yazarlar:

Emsal Altinay | Ecir Uğur Küçüksille

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 5, pp.1095-1105

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358476

pISSN:

2149-3367

Speaker Diarization

Traditional Clustering Algorithm

Deep Learning

Overlapping Speech

Computational ComplexityKonuşmacı Diyarizasyonu

Geleneksel Kümeleme Algoritması

Derin Öğrenme

Örtüşen Konuşma

Hesaplama Karmaşıklığı

Sayı

Makale detayları

Yazarlar: Emsal Altinay | Ecir Uğur Küçüksille

Yayıncı: Afyon Kocatepe University

Ana Başlıklar: Bilgisayar Yazılımı

Dergi: Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 5, pp.1095-1105

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001358476

pISSN: 2149-3367

Sayı

Yazarlar:

Emsal Altinay | Ecir Uğur Küçüksille

Yayıncı:

Afyon Kocatepe University

Ana Başlıklar:

Bilgisayar Yazılımı

Dergi:

Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi Vol.25 Issue 5, pp.1095-1105

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001358476

pISSN:

2149-3367