Text-prompted speaker verification experiments with phoneme specific MLPs

Delacretaz, Dijana Petrovska; Hennebert, Jean

doi:10.1109/icassp.1998.675380

Cited by 14 publications

(4 citation statements)

References 11 publications

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“…Several countermeasures against each type of spoofing attack have been reported. For replay attacks, we can simply use text prompting and change the prompts every time [28,29]. However, for spoofing attacks based on material generated by means of speech synthesis and voice conversion techniques, none of the reported countermeasures provide a fundamental solution [30].…”

Section: Introductionmentioning

confidence: 99%

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Shiota¹,

Villavicencio²,

Yamagishi³

et al. 2016

The Speaker and Language Recognition Workshop (Odyssey 2016)

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This paper presents an algorithm for detecting spoofing attacks against automatic speaker verification (ASV) systems. While such systems now have performances comparable to those of other biometric modalities, spoofing techniques used against them have progressed drastically. Several techniques can be used to generate spoofing materials (e.g., speech synthesis and voice conversion techniques), and detecting them only on the basis of differences at an acoustic speaker modeling level is a challenging task. Moreover, differences between "live" and artificially generated material are expected to gradually decrease in the near future due to advances in synthesis technologies. A previously proposed "voice liveness" detection framework aimed at validating whether speech signals were generated by a person or artificially created uses elementary algorithms to detect pop noise. Detection is taken as evidence of liveness. A more advanced detection algorithm has now been developed that combines single-and double-channel pop noise detection. Experiments demonstrated that this tandem algorithm detects pop noise more effectively: the detection error rate was up to 80% less that those achieved with the elementary algorithms.

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Section: Introductionmentioning

confidence: 99%

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Shiota¹,

Villavicencio²,

Yamagishi³

et al. 2016

The Speaker and Language Recognition Workshop (Odyssey 2016)

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“…Multi‐layer perceptrons are the most popular form of neural network, being used in a wide variety of recognition applications. Delacretaz and Hennebert () investigated the use of specific phoneme MLP networks for a speaker verification task. HMMs were used to extract the phoneme information from the speech data, and then each of phoneme data was classified by using an individual MLP network.…”

Section: Related Workmentioning

confidence: 99%

Speaker verification using heterogeneous neural network architecture with linear correlation speech activity detection

2013

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This paper presents a multi-level speaker verification system that uses 64 discrete Fourier transform spectrum components as input feature vectors. A speech activity detection technique is used as a pre-processing stage to identify vowel phoneme boundaries within a speech sample. A modified self-organising map (SOM) is then used to filter the speech data by using cluster information extracted from three vowels for a claimed speaker. This SOM filtering stage also provides coarse speaker verification. Finally, a second speaker verification level of three multi-layer perceptron networks classifies the filtered frames provided by the SOMs. These multi-layer perceptrons work as fine-grained vowel-based speaker verifiers. The proposed verification algorithm shows a performance of 94.54% when evaluated using 50 speakers from the Centre for Spoken Language Understanding speaker verification database. In addition, it is shown that the novel discrete Fourier transform spectrum-based linear correlation pre-processing technique, presented here, provides the system with greater robustness against changes in speech volume levels when compared with an equivalent energy frame analysis.

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“…Several countermeasures against each type of spoofing attack have been reported. We can simply use text-prompted ASVs and change prompts every time to protect against replay attacks [27,28]. However, no methods have reached a fundamental solution against the spoofing attacks using speech synthesis and voice conversion.…”

Section: Introductionmentioning

confidence: 99%

Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

Shiota

Villavicencio

Yamagishi³

et al. 2015

Interspeech 2015

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This paper proposes a novel countermeasure framework to detect spoofing attacks to reduce the vulnerability of automatic speaker verification (ASV) systems. Recently, ASV systems have reached equivalent performances equivalent to those of other biometric modalities. However, spoofing techniques against these systems have also progressed drastically. Experimentation using advanced speech synthesis and voice conversion techniques has showed unacceptable false acceptance rates and several new countermeasure algorithms have been explored to detect spoofing materials accurately. However, the countermeasures proposed so far are based on the acoustic differences between natural speech signals and artificial speech signals, expected to become gradually smaller in the near future. In this paper, we focus on voice liveness detection, which aims to validate whether the presented speech signals originated from a live human. We use the phenomenon of pop noise, which is a distortion that happens when human breath reaches a microphone, as liveness evidence. This paper proposes pop noise detection algorithms and shows through an experimental study that they can be used to discriminate live voice signals from artificial ones generated by means of speech synthesis techniques.

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Text-prompted speaker verification experiments with phoneme specific MLPs

Cited by 14 publications

References 11 publications

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Voice Liveness Detection for Speaker Verification based on a Tandem Single/Double-channel Pop Noise Detector

Speaker verification using heterogeneous neural network architecture with linear correlation speech activity detection

Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

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