# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 5 Partial Fake Speech Against Machines
### PF Attack Against Resemblyzer  
We conducted a PF speech attack on Resemblyzer, a widely used modern SR system, using the system's official open-source code [63]. **Experiment Setup. ** Using attack success rate (ASR) as the metric, we evaluated the effectiveness of a DNN-based PF speech attack against Resemblyzer. We specifically analysed the impact of the following five factors on ASR:  
* Synthetic speech generation methods: We evaluated TTS generation methods using AWS, Azure, Google Cloud, and the open-sourced tool Tortoise, and VC generation methods FreeVC, DiffVC, and ppgVC;
* The location of a fake signal within the audio file: We evaluated three options for the location of a fake signal within the audio file. All PF speeches are included in our customised dataset listed in Section 4.5. The three locations of PF speech are as follows: (1) fake segment at the beginning of audio file; (2) fake segment at the middle of audio file; and (3) fake segment at the end of audio file. Tables 1 and 2 list the attack results of the three fake segment locations within the audio file;
* The gender and dialect of the speaker, which includes both male and female speakers, and speakers of the following dialects: Welsh, Northern, midland, and Scottish. Tables 3 and 4 include the results of each gender and dialect;
* The varying lengths of the fake segments within the audio file and their effect on the detection process. Our experiment involved a total of 86 target speakers _RB_ from three different speech sources. These sources include 30 random speakers chosen from the YouTube-8M dataset, 7 random speakers from the VCTK dataset, and 49 speakers from the UK and Ireland English Dialect Speech dataset. To produce synthetic speech _RF_, we used seven different synthetic speech generation methods, including TTS and VC. TTS was generated using AWS, Azure, Google Clouds, and the open-sourced tool Tortoise. VC was generated using FreeVC, DiffVC, and ppgVC. We combined real _RB_ and synthetic speech _RF_ to generate _RP_ speech. To create _RP_ files, we concatenated a specific segment of real audio with a specific segment of synthetic audio. PF files were generated in two segments (starting with fakeaudio, followed by real audio and vice versa) and in three segments where we placed the synthetic segment in the middle of the audio file. Resemblyzer helps determine if two speech embeddings come from the same person. To set it up, we first enrolled the target speakers using their real speech samples. We then generated embeddings, which are unique numerical representations of their speech, and selected the threshold that minimises Resemblyzer's equal error rate (EER) on these speakers, using cosine similarity as the distance metric. During verification, the system compares the embedding of the evaluated utterance with the saved embedding in the database. If a synthesis attack occurs, success is achieved if the similarity between the attack and the enrolled embeddings exceeds the threshold. For training, we enrolled 10 real audio samples belonging to the same speaker, with each speaker's audio lasting over 30 s. Resemblyzer is well known and widely used in research and verification applications. **Results. ** In the Resemblyzer experiments, a total of 21,760 \(\mathbf{RP}\) audio files belonging to 86 speakers were used to perform the attack. The results show that the attack success rate of PF audio that started with four seconds of real speech followed by two seconds of fake speech (whether generated using TTS or VC methods) was far higher than with other types of PF audio that started with fake speech or embedded the fake segment in the middle of the speech. We also noticed that gender and dialect slightly impacted the attack success rate. However, some voices had a high impact on the attack success rate, although they used the same recording setup environment. Tables 1 and 2 show the averaged results of the experiment while Figure 3 visually compares the results of all TTS and VC generation methods.