Voice conversion (VC) models can be used to anonymize speech data while preserving naturalness. Existing models, however, are mainly optimized for English and other high-resource languages, and they fail to generalize well to Arabic due to its distinct phonology. In this work, we comprehensively evaluate existing zero-shot VC systems for Arabic speech, identifying limitations in pronunciation preservation. We then train an Arabic-centric model based on EZ-VC on diverse Arabic speech, and demonstrate strong performance in intelligibility, naturalness, and pronunciation preservation, while maintaining high speaker dissimilarity. Through subjective evaluation, we further demonstrate its robustness to code-switching and cross-dialectal conversion, as well as its generalization to Arabic dialects with limited representation in the training data. Our findings highlight the limitations of zero-shot VC and the importance of target-language adaptation for robust voice anonymization.
Figures & tables
Train
Source Dev
Source Test
Target
H
S
H
S
H
S
H
S
ArVoice
3.9
2
1.4
1
2.2
2
31.0
6
MASC
485.0
0
6.6
101
6.0
90
0.0
0
Casablanca
0.0
0
1.5
-
1.5
-
0.0
0
ArzEn
5.6
14
2.1
11
2.5
13
0.4
2
Mixat
3.2
6
0.2
4
0.2
4
5.9
1
Table 1: Statistics of the labeled corpora used for training, source-speaker development and test, and target-speaker partitions, reporting duration in hours (H) and speaker count (S).
Model
Intelligibility
Privacy
Naturalness
Pronunciation Distance
WER ↓
CER ↓
SIM src↓
SIM tgt↑
EER ↑
UTMOS ↑
All ↓
E ↓
U ↓
P ↓
V ↓
Vec2Wav 2.0
36.0
21.5
0.21
0.52
46.28
2.95
0.24
0.58
0.55
0.24
0.22
YourTTS
35.2
20.5
0.22
0.29
35.88
2.03
0.29
0.58
0.39
0.33
0.26
Diff-HierVC
23.2
12.0
0.29
0.33
42.65
2.54
0.24
0.44
0.37
0.23
0.21
Seed-VC
17.7
7.7
0.22
0.57
49.46
2.71
0.15
0.44
0.34
0.20
0.16
kNN-VC (baseline)
21.1
10.2
0.32
0.55
45.95
2.75
0.14
0.34
0.22
0.14
0.12
Table 2: Results for objective evaluation. Intelligibility is reported using WER and CER (%). SIM src and SIM tgt denote speaker similarity of the converted speech to the source and target speaker, respectively. ASV EER (%) is the equal error rate of an automatic speaker verification system on the converted speech. Pronunciation distance is measured as JSD between phonetic posteriorgrams, across emphatics (E), uvulars (U), pharyngeals (P), vowels (V), and all phonemes.
Models
I ↑
S ↑
N ↑
A ↑
P ↑
Q ↑
Vec2Wav 2.0
4.38
4.15
3.95
4.08
4.52
4.12
Seed-VC
4.70
4.18
4.09
4.27
4.64
4.35
kNN-VC (baseline)
4.81
3.96
4.39
4.62
4.69
4.51
kNN-VC (AR+EN+FR)
4.82
3.93
4.43
4.65
4.63
4.49
EZ-VC (baseline)
4.34
4.19
3.68
3.81
4.27
4.04
EZ-VC (AR+EN+FR)
4.84
4.02
4.53
4.67
4.77
4.55
Table 3: Results for Human Study 1 showing MOS for intelligibility (I), speaker dissimilarity (S), naturalness (N), accentedness (A), style preservation (P), and audio quality (Q).
Models
I ↑
S ↑
N ↑
A ↑
P ↑
Q ↑
(a) Source Monolingual vs. Code-switching (CS)
Egyptian mono
4.95
3.75
4.50
4.80
4.80
4.69
Egyptian CS
4.87
3.84
4.52
4.70
4.67
4.61
Tunisian mono
4.86
3.53
4.42
4.66
4.72
4.51
Tunisian CS
4.82
3.76
4.47
4.51
4.65
4.56
Emirati mono
4.93
4.86
4.59
4.75
4.69
4.66
Table 4: Results for Human Study 2 showing MOS for intelligibility (I), speaker dissimilarity (S), naturalness (N), accentedness (A), style preservation (P), and audio quality (Q). DA denotes dialectal Arabic, and DA x → DA x /DA x → DA y indicate same/different source → target dialects, respectively.
Department of Computational Linguistics, University of Zurich, Switzerland · Idiap Research Institute, Switzerland · Brno University of Technology, Czech Republic