Deepfake speech utterances can be forged by replacing one or more words in a bona fide utterance with semantically different words synthesized with speech-generative models. While a dedicated synthetic word detector could be developed, we developed a cost-effective method that fine-tunes a pre-trained Whisper model to detect synthetic words while transcribing the input utterance via next-token prediction. We further investigate using partially vocoded utterances as the fine-tuning data, thus reducing the cost of data collection. Our experiments demonstrate that, on in-domain test data, the fine-tuned Whisper yields low synthetic-word detection error rates and transcription error rates. On out-of-domain test data with synthetic words produced with unseen speech-generative models, the fine-tuned Whisper remains on par with a dedicated ResNet-based detection model; however, the overall performance degradation calls for strategies to improve its generalization capability.
Figures & tables
Figure 1: Illustration of fine-tuned Whisper for speech-to-text transcription and synthetic word detection. <TOF> and <EOF> denote tokens surrounding synthetic word.
Name
#. of utterances
Language(s)
Domain
Generator of synthetic words
Train data
Ft.Voc
60,596
en, es, fr, it, de
Audiobook
HiFi-GAN, NSF, NSF+GAN, WaveGlow, WORLD, GL
Ft.TTS
60,596
en
Audiobook
JETS, YourTTS, XTTS, SoVITS, CosyVoice, ElevenLab
Ft.V+T
60,596
en, es, fr, it, de
Audiobook
All above
Test data
E.Voc
3,000
en, es, fr, it, de
Audiobook
HiFi-GAN, NSF, NSF+GAN, WaveGlow, WORLD, GL
E.TTS
3,000
en
Audiobook
JETS, YourTTS, XTTS, SoVITS, CosyVoice, ElevenLab
E.AV1M
3,000
en
Youtube
YourTTS, VITS
Table 1: Statistics of fine-tuning ( Ft. ) and test ( E. ) data sets. Generators using full speech-generative systems are in italics.
Data set
WER(%) Whisper
FAR (%)
FRR (%)
Test
Fine-tune
Whisper
ResNet
Whisper
ResNet
E.Voc
Pre-trained
23.89
Ft.Voc
0.87
7.22
7.15
0.52
3.81
Ft.V+T
2.18
8.45
9.80
1.01
5.32
Ft.TTS
35.11
18.86
76.42
78.59
9.08
E.TTS
Pre-trained
8.13
Table 2: Results on E.Voc and E.TTS . Bold text indicates the best result in each column and each test set.
Figure 2: Analysis of synthetic-word detection error rates based on word duration. Black and red profiles correspond to fine-tuned Whisper using Ft.Voc and Ft.TTS , respectively.
FAR (%)
FRR (%)
Ft.Voc
Ft.TTS
Ft.Voc
Ft.TTS
en
8.38
46.28
0.50
40.97
fr
10.41
11.54
0.78
91.19
es
5.59
10.76
0.36
89.36
de
5.47
8.97
0.40
91.80
it
6.30
13.03
0.53
88.12
Table 3: Decomposed results over languages using pre-trained Whisper (left) or fine-tuned on Ft.Voc (right).
Data set
WER(%) Whisper
FAR (%)
FRR (%)
Test
Fine-tune
Whisper
ResNet
Whisper
ResNet
E.AV1M
Pre-trained
14.72
Ft.Voc
23.17
39.98
33.70
7.70
17.69
Ft.V+T
21.23
15.98
27.63
39.42
27.04
Ft.TTS
20.47
16.04
11.14
59.97
36.44
E.PE
Pre-trained
3.64
Table 4: Results on test sets manipulated using DNN-based speech synthesis and editing systems. Within each column, value with darker shading indicates worse performance.