Unsupervised Representation Learning Using Convolutional Restricted Boltzmann Machine for Spoof Speech Detection

Sailor, Hardik B.; Kamble, Madhu R.; Patil, Hemant A.

doi:10.21437/interspeech.2017-1393

Cited by 10 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before the recent boom of self-supervised models, there is a pioneering work using conditional Restricted Bolzman Machine (ConvRBM) as a trainable front end [76]. It showed competitive performance on the ASVspoof 2015 database.…”

Section: Front End: Dnn-based Self-supervised Training Approachmentioning

confidence: 99%

A Practical Guide to Logical Access Voice Presentation Attack Detection

Wang¹,

Yamagishi²

2022

Preprint

View full text Add to dashboard Cite

Voice-based human-machine interfaces with an automatic speaker verification (ASV) component are commonly used in the market. However, the threat from presentation attacks is also growing since attackers can use recent speech synthesis technology to produce a naturalsounding voice of a victim. Presentation attack detection (PAD) for ASV, or speech anti-spoofing, is therefore indispensable. Research on voice PAD has seen significant progress since the early 2010s, including the advancement in PAD models, benchmark datasets, and evaluation campaigns. This chapter presents a practical guide to the field of voice PAD, with a focus on logical access attacks using text-to-speech and voice conversion algorithms and spoofing countermeasures based on artifact detection. It introduces the basic concept of voice PAD, explains the common techniques, and provides an experimental study using recent methods on a benchmark dataset. Code for the experiments is open-sourced.

show abstract

Section: Front End: Dnn-based Self-supervised Training Approachmentioning

confidence: 99%

A Practical Guide to Logical Access Voice Presentation Attack Detection

Wang¹,

Yamagishi²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Deep learning methods were also frequently applied in PAD tasks. Unlike conventional classifiers, deep learning is one of the machine learning that is composed of networks capable of learning without supervision from labeled data [39,104]. From recent works, it is found that deep learning classifiers such as DNN [120], RNN [30], and CNN [88] are capable of automatic feature abstraction in which more informative features can be identified.…”

Section: Classifiersmentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

View full text Add to dashboard Cite

The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

show abstract

“…Bottleneck features extracted from the DNN hidden layers were also used with GMM classifier in [102]. In [103], the Convolutional Restricted Boltzmann Machine (ConvRBM) is used for auditory filterbank learning that performed better than traditionally handcrafted filterbanks. The study [103] shows that ConvRBM learns better low-frequency subband filters on ASVspoof 2015 dataset than on TIMIT.…”

Section: ) Representation Learning Approachesmentioning

confidence: 99%

“…In [103], the Convolutional Restricted Boltzmann Machine (ConvRBM) is used for auditory filterbank learning that performed better than traditionally handcrafted filterbanks. The study [103] shows that ConvRBM learns better low-frequency subband filters on ASVspoof 2015 dataset than on TIMIT. Supervised auditory filterbank learning using DNN was also studied in [104].…”

Section: ) Representation Learning Approachesmentioning

confidence: 99%

Advances in anti-spoofing: from the perspective of ASVspoof challenges

Kamble

Sailor

Patil

et al. 2020

SIP

Self Cite

View full text Add to dashboard Cite

In recent years, automatic speaker verification (ASV) is used extensively for voice biometrics. This leads to an increased interest to secure these voice biometric systems for real-world applications. The ASV systems are vulnerable to various kinds of spoofing attacks, namely, synthetic speech (SS), voice conversion (VC), replay, twins, and impersonation. This paper provides the literature review of ASV spoof detection, novel acoustic feature representations, deep learning, end-to-end systems, etc. Furthermore, the paper also summaries previous studies of spoofing attacks with emphasis on SS, VC, and replay along with recent efforts to develop countermeasures for spoof speech detection (SSD) task. The limitations and challenges of SSD task are also presented. While several countermeasures were reported in the literature, they are mostly validated on a particular database, furthermore, their performance is far from perfect. The security of voice biometrics systems against spoofing attacks remains a challenging topic. This paper is based on a tutorial presented at APSIPA Annual Summit and Conference 2017 to serve as a quick start for those interested in the topic.

show abstract

Unsupervised Representation Learning Using Convolutional Restricted Boltzmann Machine for Spoof Speech Detection

Cited by 10 publications

References 27 publications

A Practical Guide to Logical Access Voice Presentation Attack Detection

A Practical Guide to Logical Access Voice Presentation Attack Detection

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Advances in anti-spoofing: from the perspective of ASVspoof challenges

Contact Info

Product

Resources

About