311 research outputs found
Anti-spoofing Methods for Automatic SpeakerVerification System
Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 66
Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering
Voice activity detection (VAD) is an important pre-processing step for speech
technology applications. The task consists of deriving segment boundaries of
audio signals which contain voicing information. In recent years, it has been
shown that voice source and vocal tract system information can be extracted
using zero-frequency filtering (ZFF) without making any explicit model
assumptions about the speech signal. This paper investigates the potential of
zero-frequency filtering for jointly modeling voice source and vocal tract
system information, and proposes two approaches for VAD. The first approach
demarcates voiced regions using a composite signal composed of different
zero-frequency filtered signals. The second approach feeds the composite signal
as input to the rVAD algorithm. These approaches are compared with other
supervised and unsupervised VAD methods in the literature, and are evaluated on
the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show
that the proposed ZFF-based methods perform comparable to state-of-art VAD
methods and are more invariant to added degradation and different channel
characteristics.Comment: Accepted at Interspeech 202
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