Detection of AI Synthesized Hindi Speech
This work addresses the need for audio forensics in Hindi speech to prevent misuse of synthetic speech, though it is incremental as it applies existing detection techniques to a new language.
The paper tackled the problem of detecting AI-synthesized Hindi speech, which lacks prior attention, by proposing a method using features like Bicoherence Phase and MFCCs with machine learning and deep neural networks, achieving accuracies of 99.83% with VGG16 and 99.99% with a custom CNN.
The recent advancements in generative artificial speech models have made possible the generation of highly realistic speech signals. At first, it seems exciting to obtain these artificially synthesized signals such as speech clones or deep fakes but if left unchecked, it may lead us to digital dystopia. One of the primary focus in audio forensics is validating the authenticity of a speech. Though some solutions are proposed for English speeches but the detection of synthetic Hindi speeches have not gained much attention. Here, we propose an approach for discrimination of AI synthesized Hindi speech from an actual human speech. We have exploited the Bicoherence Phase, Bicoherence Magnitude, Mel Frequency Cepstral Coefficient (MFCC), Delta Cepstral, and Delta Square Cepstral as the discriminating features for machine learning models. Also, we extend the study to using deep neural networks for extensive experiments, specifically VGG16 and homemade CNN as the architecture models. We obtained an accuracy of 99.83% with VGG16 and 99.99% with homemade CNN models.