I-vectorlerin Maskeleme Yoluyla Dayanıklılığının Arttırılması: Sentetik Konuşma Tespitinde Bir Vaka Çalışması

Year 2024, Volume: 29 Issue: 1, 191 - 204, 22.04.2024

Barış Aydın , Gökay Dişken

https://doi.org/10.17482/uumfd.1311113

Abstract

Konuşmacı tanıma sistemleri için güvenlik hayati önem taşımaktadır. Geçtiğimiz yıllarda, sahte konuşma saldırılarının bu sistemleri kandırabildiği ortaya konmuştur. Bu durumu önlemek amacı ile sahte konuşma tespit sistemleri geliştirilmiştir. Bu tür sistemler bazı durumlarda oldukça yüksek performans sergilese de, gürültü altında performansları kötüleşmektedir. Geleneksel konuşma iyileştirme yöntemleri performansı artırmak bir yana, daha da kötüleştirmektedir. Bu çalışmada, konvolüsyonel sinir ağı yapısı kullanılarak elde edilen maskenin gürültü etkisini azaltmaktaki performansı incelenmiştir. Maske, spektrogramın gürültülü bölgelerini bastırmakta ve bu spektrogramdan elde edilen i-vectorleri gürbüz hale getirmekte kullanılmıştır. ASVspoof 2015 veri tabanı ve üç farklı gürültü tipi ile gerçekleştirilen testlerde önerilen sistemin geleneksel sistemlerden daha üstün olduğu gösterilmiştir. Ancak eğitim aşamasında karşılaşılmayan gürültü tiplerinde performans kaybı olmaktadır.

Keywords

derin öğrenme, evrişimli sinir ağı, sahte konuşma tanıma, konuşmacı tanıma, gürbüz öznitelikler

Project Number

121E057

INCREASING ROBUSTNESS OF I-VECTORS VIA MASKING: A CASE STUDY IN SYNTHETIC SPEECH DETECTION

Abstract

Ensuring security in speaker recognition systems is crucial. In the past years, it has been demonstrated that spoofing attacks can fool these systems. In order to deal with this issue, spoof speech detection systems have been developed. While these systems have served with a good performance, their effectiveness tends to degrade under noise. Traditional speech enhancement methods are not efficient for improving performance, they even make it worse. In this research paper, performance of the noise mask obtained via a convolutional neural network structure for reducing the noise effects was investigated. The mask is used to suppress noisy regions of spectrograms in order to extract robust i-vectors. The proposed system is tested on the ASVspoof 2015 database with three different noise types and accomplished superior performance compared to the traditional systems. However, there is a loss of performance in noise types that are not encountered during training phase.

Keywords

deep learning, convolutional neural network, spoof detection, speaker recognition, robust features

Supporting Institution

TÜBİTAK

Project Number

121E057

Thanks

This work was supported by TÜBİTAK (Project No: 121E057).

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