# Detecting Deepfake Voice Using Explainable Deep Learning Techniques
## 5 Experiment
### Audio Interpretability  
Currently, interpretability is focused on a visual method, i.e., a heatmap on a spectrogram, to interpret the model. With the visual approach, diverse characteristics of the model's tendency, depending on XAI methods and data types, have been found. Through visual interpretation, the user can confirm what the model is focusing on. However, visual interpretation has certain limitations in intuitiveness and in assisting decision-making. Using the Griffin-Lim algorithm, the attribution scores of each model and XAI method were recomposed back to audio. For the CNN-only model, because of the tendency of XAI methods to show dependencies on energy-like formants, most of the original voice was recovered during the conversion from the attribution score. The transcript of the original voice can be recognized from the recomposed voice as well.  
The attribution scores of [PERSON] and [PERSON] on the CNN-LSTM-based model showed less sensitivity to the energy, losing highlights on formants. Consequently, the  
Figure 8: Spectrogram of LJSpeech “The wildest and most cut-throat looking of the lot, which proves that he could be grateful for kindness, and was not all bad.” (**a**) Deepfake voice. (**b**) Real voice.  
Figure 7: LJSpeech attribution score with CNN-LSTM-perm model. (**a**) [PERSON] deepfake voice. (**b**) Integrated gradients deepfake voice. (**c**) [PERSON] deepfake voice. (**d**) [PERSON] real voice. (**e**) Integrated gradients real voice. (**f**) [PERSON] real voice.  
transcript was unrecognizable on the recomposed voice. In the case of the recomposed voice of [PERSON] and [PERSON] on the CNN-LSTM model, because of the lack of sensitivity on formants 1, 2, and 3, the transcript of the speech was unrecognizable.  
However, with the recomposed voice, visually unrecognizable characteristics, general pitch variance, and rhythm of the speech were revealed with the removal of formant dependency on the attribution score of the CNN-LSTM model. Hence, the LRP attribution score on the recomposed voice showed relatively distinct differences between the voiced and unvoiced sections.  
Rhythm and pitch variance differences on a Deep-Taylor-based recomposed voice can assist in distinguishing a human voice from a deepfake voice. The features showed significantly high variances in a human voice compared to the deepfake, which is presumably considered an accent, which is a feature that the generative model did not train for. Figure 9 shows the top 10% of the amplitude of the recomposed speech as a visual reference, for the comparison of pitch and rhythm variances between the deepfake and human voice.  
Based on the audio interpretation, i.e., the spectrograms in Figure 8, energy distribution on frequency 2 kHz showed distensibility. The deepfake voice showed a relatively flat pattern compared to a human voice, while the human voice showed randomness. This phenomenon was assumed to be triggered by the absence of training on accents while generating training data from the LJSpeech through Tacotron TTS. This absence resulted in a flat rhythm and accent.