System Detail
wespeaker/ res34-voxceleb
wespeaker_resnet34_voxceleb
This page shows the global rank, scenario ranks, and trial-set results for this submission.
Global Position
#7/18
Public ranking position among reviewed full-core submissions.
Scenario-Macro EER
12.0516
Scenario-Macro minDCF
0.477054
Coverage
26/26
0 scenarios ranked #1
0 scenarios in top 3
Public Listing
Ranked Publicly
This full-core result is approved and included in the public ranking.
Model Provenance
Source and architecture
- Displayed name:
wespeaker/res34-voxceleb - Source: WeSpeaker
- Architecture: ResNet
- Submitted alias:
wespeaker-voxceleb-resnet34-LM - Created at:
2026-05-29T14:51:34.068325+00:00
Training And Links
Training data and references
- Training data: VoxCeleb2 dev, 5,994 speakers
- Training setup: ResNet34-TSTP-emb256 r-vector, large-margin fine-tuned; official card reports 6.63M parameters and 4.55G FLOPs.
- Submission mode:
full-core - Paper: Deep Residual Learning for Image Recognition
Higher-Ranked Scenarios
Scenario ranks above the global position
Show 3 supporting trial sets
- GLOBE: #4 on its trial-set ranking.
- Lombard Grid Lombard: #5 on its trial-set ranking.
- ESD: #6 on its trial-set ranking.
Lower-Ranked Scenarios
Scenario ranks below the global position
Show 3 supporting trial sets
- CHiME-6 Domestic Far-Field: #10 on its trial-set ranking.
- GSC Short: #10 on its trial-set ranking.
- AliMeeting Overlap: #10 on its trial-set ranking.
Scenario Rankings
Full ranking across scenarios
This table shows where the model sits on each scenario, using source-balanced scenario scores and the linked trial sets as evidence.
| Scenario | Rank | Lens | Evidence | Score |
|---|---|---|---|---|
|
Aging Robustness
How stable identity representations remain across age-derived and longitudinal recording gaps.
|
#6/18
Competitive
|
Speaker aging and time gaps
1.4319 EER from leader
|
3.5015
0.146062 minDCF
|
|
|
Speaking-Style Robustness
Whether the model can preserve identity across emotion-driven change, whispered speech, and noise-induced Lombard speaking style.
|
#6/18
Competitive
|
Speaking style shift
2.2464 EER from leader
|
5.0359
0.303656 minDCF
|
|
|
In-The-Wild Robustness
How strong the model is on unconstrained celebrity and media speech across official CN-Celeb and VoxCeleb protocols.
|
#6/18
Competitive
|
Open-domain media speech
1.3742 EER from leader
|
6.5576
0.240625 minDCF
|
|
|
Overlap Robustness
How well speaker identity survives light, mid, and heavy overlap in both meeting and domestic recordings.
|
#7/18
Competitive
|
Overlapping speakers
3.8031 EER from leader
|
13.9414
0.591408 minDCF
|
|
|
Cross-Lingual Robustness
How well speaker identity survives enrollment-test language mismatch.
|
#8/18
Competitive
|
Language mismatch
1.0856 EER from leader
|
5.5532
0.361444 minDCF
|
|
|
Noise / Reverb Robustness
How reliably the model preserves identity when room acoustics, distractor noise, and microphone placement deviate from an easier in-corpus reference condition.
|
#8/18
Competitive
|
Noise and reverberation
3.3480 EER from leader
|
5.9280
0.181180 minDCF
|
|
|
Accent / Dialect Robustness
How reliably the model tracks identity across accent and dialect mismatch.
|
#8/18
Competitive
|
Accent and dialect variation
1.9922 EER from leader
|
22.4273
0.700394 minDCF
|
|
|
Genre-Shift Robustness
Whether performance holds when CN-Celeb enrollment and test speech come from different source genres.
|
#8/18
Competitive
|
Source-genre variation
12.2400 EER from leader
|
26.5125
0.822805 minDCF
|
|
|
Distance Robustness
How much performance changes across meeting and domestic distance mismatch.
|
#9/18
Competitive
|
Distance mismatch
3.7329 EER from leader
|
12.9967
0.561170 minDCF
|
|
|
Short-Duration Robustness
How much performance holds up when speech evidence is limited by duration.
|
#9/18
Competitive
|
Short-duration speech
2.3936 EER from leader
|
14.9080
0.610039 minDCF
|
|
|
Channel / Device Robustness
How resilient the model is to device and channel mismatch.
|
#10/18
Needs work
|
Device and channel variation
8.8947 EER from leader
|
15.2050
0.728816 minDCF
|
Variant Compare
Compare ResNet variants
Expand this section to compare checkpoints in the same architecture group by source, training data, training setup, and leaderboard result.
Show
Compare ResNet variants
Expand this section to compare checkpoints in the same architecture group by source, training data, training setup, and leaderboard result.
| Variant | Source | Training Data | Training Setup | Global Rank | Macro EER | Macro minDCF | Open |
|---|---|---|---|---|---|---|---|
| wespeaker/res152-voxceleb | WeSpeaker | VoxCeleb2 dev, 5,994 speakers | ResNet152-TSTP-emb256 r-vector, ArcMargin, 150-epoch VoxCeleb2 recipe with speed perturbation and MUSAN/RIRS augmentation. |
#4
ranked
|
11.2275 | 0.442748 | Open |
| wespeaker/res293-voxceleb | WeSpeaker | VoxCeleb2 dev, 5,994 speakers | ResNet293-TSTP-emb256 r-vector, large-margin fine-tuned; official card reports 28.62M parameters and 28.10G FLOPs. |
#5
ranked
|
11.3130 | 0.439607 | Open |
|
wespeaker/res34-voxceleb
Current
|
WeSpeaker | VoxCeleb2 dev, 5,994 speakers | ResNet34-TSTP-emb256 r-vector, large-margin fine-tuned; official card reports 6.63M parameters and 4.55G FLOPs. |
#7
ranked
|
12.0516 | 0.477054 | Open |
| wespeaker/res34-cnceleb | WeSpeaker | CN-Celeb train | ResNet34 r-vector with TSTP pooling and large-margin fine-tuning on the CN-Celeb WeSpeaker recipe. |
#14
ranked
|
14.4910 | 0.607964 | Open |
Trial-Set Results
Raw ranking by trial set
Expand this section to inspect the detailed trial-set rankings.
Show
Raw ranking by trial set
Expand this section to inspect the detailed trial-set rankings.
| Trial Set | Rank | Scenarios | Trials | Score |
|---|---|---|---|---|
|
TidyVoiceX2-ASV
TidyVoiceX2-ASV
|
#8/18
|
Cross-lingual
|
200000 |
5.5532
0.361444 minDCF
|
|
HI-MIA
HI-MIA
|
#9/18
|
Short-duration
|
660000 |
7.0523
0.323703 minDCF
|
|
GSC Short
Google Speech Commands
|
#10/18
|
Short-duration
|
220000 |
13.2190
0.705995 minDCF
|
|
GLOBE
GLOBE
|
#4/18
|
Accent/dialect
|
56848 |
25.0600
0.531734 minDCF
|
|
CN-Celeb
CN-Celeb
|
#7/18
|
In-the-wild
|
3484292 |
11.9718
0.411595 minDCF
|
|
CN-Celeb Genre
CN-Celeb
|
#8/18
|
Genre shift
|
440000 |
26.5125
0.822805 minDCF
|
|
CN-Celeb Short
CN-Celeb
|
#7/18
|
Short-duration
|
546964 |
25.4223
0.914387 minDCF
|
|
VoxCeleb1-O
VoxCeleb1
|
#8/18
|
In-the-wild
|
37611 |
0.8188
0.052968 minDCF
|
|
VoxCeleb1-E
VoxCeleb1
|
#8/18
|
In-the-wild
|
579818 |
0.9347
0.057497 minDCF
|
|
VoxCeleb1-H
VoxCeleb1
|
#7/18
|
In-the-wild
|
550894 |
1.6768
0.098504 minDCF
|
|
VoxCeleb Short
VoxCeleb1
|
#7/18
|
Short-duration
|
394724 |
13.9382
0.496071 minDCF
|
|
3D-Speaker Device
3D-Speaker
|
#9/18
|
Channel/device
|
180000 |
19.8267
0.860520 minDCF
|
|
3D-Speaker Distance
3D-Speaker
|
#9/18
|
Distance
|
175163 |
18.6760
0.836862 minDCF
|
|
3D-Speaker Dialect
3D-Speaker
|
#8/18
|
Accent/dialect
|
180000 |
19.7947
0.869053 minDCF
|
|
FFSVC 2022 Cross-Channel
FFSVC 2022
|
#8/18
|
Channel/device
|
72000 |
10.5833
0.597111 minDCF
|
|
FFSVC 2022 Cross-Domain
FFSVC 2022
|
#8/18
|
Distance
|
66546 |
10.7439
0.612159 minDCF
|
|
Whisper40 Whisper
Whisper40
|
#9/18
|
Speaking style
|
17600 |
11.4375
0.645688 minDCF
|
|
Lombard Grid Lombard
Lombard Grid
|
#5/18
|
Speaking style
|
29524 |
0.1490
0.010991 minDCF
|
|
VOiCES Noise/Reverb
VOiCES
|
#8/18
|
Noise/reverb
|
55000 |
5.9280
0.181180 minDCF
|
|
CHiME-6 Domestic Far-Field
CHiME-6
|
#10/18
|
Distance
|
36487 |
13.6871
0.500573 minDCF
|
|
CHiME-6 Overlap
CHiME-6
|
#9/18
|
Overlap
|
39600 |
17.8889
0.651583 minDCF
|
|
ESD
ESD
|
#6/18
|
Speaking style
|
437408 |
3.5211
0.254288 minDCF
|
|
AliMeeting Near/Far
AliMeeting
|
#9/18
|
Distance
|
220000 |
8.8800
0.295085 minDCF
|
|
AliMeeting Overlap
AliMeeting
|
#10/18
|
Overlap
|
165000 |
9.9940
0.531233 minDCF
|
|
VoxKnesset
VoxKnesset
|
#6/18
|
Aging
|
158312 |
4.9242
0.199224 minDCF
|
|
VoxPopuli Aging
VoxPopuli
|
#7/18
|
Aging
|
146575 |
2.0788
0.092901 minDCF
|