Extraction of Octave Spectra Information for Spoofing Attack Detection

This article focuses on extracting information from the octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by le...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2019-12, Vol.27 (12), p.2373-2384
Hauptverfasser: Yang, Jichen, Das, Rohan Kumar, Zhou, Nina
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This article focuses on extracting information from the octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by level in a multi-level manner. We then derive a novel feature referred to as constant-Q multi-level coefficient (CMC) based on proposed MLT. The proposed feature is evaluated on synthetic as well as replay speech detection studies on ASVspoof 2015 and ASVspoof 2017 version 2.0 database, respectively. We find the proposed CMC feature outperforms the conventional constant-Q cepstral coefficient based long-term feature obtained from linear power spectrum after uniform resampling. This depicts the usefulness of MLT to extract salient artifacts from octave power spectrum. Further, the proposed CMC feature performs better than the existing the well known other state-of-the-art systems for spoofing attack detection that showcases its importance.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2019.2946897