Anti-Spoofing Voice Commands

Author(s):  
Cui Zhao ◽  
Zhenjiang Li ◽  
Han Ding ◽  
Wei Xi ◽  
Ge Wang ◽  
...  

This paper presents an anti-spoofing design to verify whether a voice command is spoken by one live legal user, which supplements existing speech recognition systems and could enable new application potentials when many crucial voice commands need a higher-standard verification in applications. In the literature, verifying the liveness and legality of the command's speaker has been studied separately. However, to accept a voice command from a live legal user, prior solutions cannot be combined directly due to two reasons. First, previous methods have introduced various sensing channels for the liveness detection, while the safety of a sensing channel itself cannot be guaranteed. Second, a direct combination is also vulnerable when an attacker plays a recorded voice command from the legal user and mimics this user to speak the command simultaneously. In this paper, we introduce an anti-spoofing sensing channel to fulfill the design. More importantly, our design provides a generic interface to form the sensing channel, which is compatible to a variety of widely-used signals, including RFID, Wi-Fi and acoustic signals. This offers a flexibility to balance the system cost and verification requirement. We develop a prototype system with three versions by using these sensing signals. We conduct extensive experiments in six different real-world environments under a variety of settings to examine the effectiveness of our design.

Author(s):  
Conrad Bernath ◽  
Aitor Alvarez ◽  
Haritz Arzelus ◽  
Carlos David Martínez

Author(s):  
Sheng Li ◽  
Dabre Raj ◽  
Xugang Lu ◽  
Peng Shen ◽  
Tatsuya Kawahara ◽  
...  

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 130-135
Author(s):  
Christian Deuerlein ◽  
Moritz Langer ◽  
Julian Seßner ◽  
Peter Heß ◽  
Jörg Franke

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 634
Author(s):  
Alakbar Valizada ◽  
Natavan Akhundova ◽  
Samir Rustamov

In this paper, various methodologies of acoustic and language models, as well as labeling methods for automatic speech recognition for spoken dialogues in emergency call centers were investigated and comparatively analyzed. Because of the fact that dialogue speech in call centers has specific context and noisy, emotional environments, available speech recognition systems show poor performance. Therefore, in order to accurately recognize dialogue speeches, the main modules of speech recognition systems—language models and acoustic training methodologies—as well as symmetric data labeling approaches have been investigated and analyzed. To find an effective acoustic model for dialogue data, different types of Gaussian Mixture Model/Hidden Markov Model (GMM/HMM) and Deep Neural Network/Hidden Markov Model (DNN/HMM) methodologies were trained and compared. Additionally, effective language models for dialogue systems were defined based on extrinsic and intrinsic methods. Lastly, our suggested data labeling approaches with spelling correction are compared with common labeling methods resulting in outperforming the other methods with a notable percentage. Based on the results of the experiments, we determined that DNN/HMM for an acoustic model, trigram with Kneser–Ney discounting for a language model and using spelling correction before training data for a labeling method are effective configurations for dialogue speech recognition in emergency call centers. It should be noted that this research was conducted with two different types of datasets collected from emergency calls: the Dialogue dataset (27 h), which encapsulates call agents’ speech, and the Summary dataset (53 h), which contains voiced summaries of those dialogues describing emergency cases. Even though the speech taken from the emergency call center is in the Azerbaijani language, which belongs to the Turkic group of languages, our approaches are not tightly connected to specific language features. Hence, it is anticipated that suggested approaches can be applied to the other languages of the same group.


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