There is a great concern regarding the misuse of deepfake speech technology to synthesize a real person’s voice. Therefore, developing speech-security systems capable of detecting deepfake speech remains paramount in safeguarding against such misuse. Although various speech features and methods have been proposed, their potential for distinguishing between genuine and deepfake speech remains unclear. Since speech-pathological features with deep learning are widely used to assess unnaturalness in disordered voices associated with voice-production mechanisms, we investigated the potential of eleven speech-pathological features for distinguishing between genuine and deepfake speech, i.e., jitter (three types), shimmer (four types), harmonics-to-noise ratio, cepstral-harmonics-to-noise ratio, normalized noise energy, and glottal-to-noise excitation ratio. This paper proposes a method of combining two models on the basis of two different dimensions of speech-pathological features to greatly improve the effectiveness of deepfake speech detection, along with mel-spectrogram features, to enhance detection efficiency. We evaluated the proposed method on the datasets of the Automatic Speaker Verification Spoofing and Countermeasures Challenges ASVspoof 2019 and 2021. The results indicate that the proposed method outperforms the baselines in terms of accuracy, recall, F1-score, and F2-score, achieving 95.06, 99.46, 97.30, and 98.59%, respectively, on the ASVspoof 2019 dataset. It also surpasses the baselines on the ASVspoof 2021 dataset in terms of recall, F1-score, F2-score, and equal error rate, achieving 99.96, 96.65, 98.18, and 15.97%, respectively.

The detection of deepfake speech has become a significant research area due to rapid advancements in generative AI for speech synthesis. These technologies pose significant security risks in applications such as biometric authentication, voice-controlled systems, and automatic speaker verification (ASV) systems. Therefore, enhancing the detection capabilities of such applications is essential to mitigate potential threats. This study investigates perceptual speech-pathological features, which are commonly used to evaluate the unnaturalness of voice disorders in clinical settings, as potential indicators for detecting deepfake speech. Specifically, the timbral attributes of hardness, depth, brightness, roughness, sharpness, warmth, boominess, and reverberation are examined. The analysis reveals that these attributes provide meaningful distinctions between genuine and synthetic speech. Furthermore, the detection performance is enhanced by extending the dimensional representation of timbral attributes, enabling a more comprehensive characterization of the speech signal. This paper proposes a method that combines two models: one utilizing the different dimensions of speech-pathological features with a deep neural network (DNN), and another employing a gammatone filterbank model that simulates the auditory processing mechanism of the human cochlea with ResNet-18 architecture, improving deepfake speech detection. The proposed method is evaluated on the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof) 2019 dataset. Experimental results demonstrate that the proposed approach outperforms baseline models in terms of Equal Error Rate (EER), achieving an EER of $5.93$%.