Automatic speech generation algorithms, enhanced by deep learning techniques, enable an increasingly seamless and immediate machine-to-human interaction. As a result, the latest generation of phone-calling bots sounds more convincingly human than previous generations. The application of this technology has a strong social impact in terms of privacy issues (e.g., in customer-care services), fraudulent actions (e.g., social hacking) and erosion of trust (e.g., generation of fake conversation). For these reasons, it is crucial to identify the nature of a speaker, as either a human or a bot. In this paper, we propose a speech classification algorithm based on Convolutional Neural Networks (CNNs), which enables the automatic classification of human vs non-human speakers from the analysis of short audio excerpts. We evaluate the effectiveness of the proposed solution by exploiting a real human speech database populated with audio recordings from various sources, and automatically generated speeches using state-of-the-art text-to-speech generators based on deep learning (e.g., Google WaveNet).

Hello? Who Am i Talking to? A Shallow CNN Approach for Human vs. Bot Speech Classification

Lieto A.;Moro D.;Devoti F.;Parera C.;Lipari V.;Bestagini P.;Tubaro S.
2019

Abstract

Automatic speech generation algorithms, enhanced by deep learning techniques, enable an increasingly seamless and immediate machine-to-human interaction. As a result, the latest generation of phone-calling bots sounds more convincingly human than previous generations. The application of this technology has a strong social impact in terms of privacy issues (e.g., in customer-care services), fraudulent actions (e.g., social hacking) and erosion of trust (e.g., generation of fake conversation). For these reasons, it is crucial to identify the nature of a speaker, as either a human or a bot. In this paper, we propose a speech classification algorithm based on Convolutional Neural Networks (CNNs), which enables the automatic classification of human vs non-human speakers from the analysis of short audio excerpts. We evaluate the effectiveness of the proposed solution by exploiting a real human speech database populated with audio recordings from various sources, and automatically generated speeches using state-of-the-art text-to-speech generators based on deep learning (e.g., Google WaveNet).
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
978-1-4799-8131-1
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1126641
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