InterBias-SV: Compound Conditions in Speaker Verification

Kamel Kamel; Hridoy Sankar Dutta; Keshav Sood; Sunil Aryal

cs.SDSep 29, 2026

InterBias-SV: Compound Conditions in Speaker Verification

Authors: Kamel Kamel, Hridoy Sankar Dutta, Keshav Sood, Sunil Aryal

Organizations: School of Information Technology, Deakin University Waurn Ponds, VIC, Australia

Abstract

Speaker verification systems encounter combinations of noise, channel distortion, and changes in speech. Evaluating each condition separately does not establish whether their effects add. InterBias-SV organises this question around a four-term comparison: joint error, two marginal errors, and a common reference. Its results artefact contains 4,068 scored records across 17 experiments, 12 encoder labels, and six speech corpora, totalling 12 million trial evaluations. Three experiment families contain the same-corpus terms needed to compute additive contrasts. For labels assigned to speaker-trained encoders, their mean contrasts are +0.0026, +0.0088, and +0.0024 in equal error rate (EER), with larger variation across settings. These descriptive averages do not establish equivalence to additivity: trial matching, checkpoint identity, and parts of the condition metadata remain unverified. We also examine two interpretation problems. Near-chance EER can make additive predictions difficult to interpret, but chance performance is not a hard EER ceiling, and correlation with the prediction does not identify a saturation mechanism. Ratios of demographic gaps are unstable when their clean reference is near zero; absolute gaps provide a more direct summary. The benchmark provides condition definitions, analysis scripts, and explicit requirements for interpretable compound-condition comparisons, while separating recomputable summaries from claims that require further experimental validation.

Figures & tables

Resource	Evaluation emphasis
VoxCeleb / VoxSRC	In-the-wild verification and challenge protocols
SUPERB	Downstream evaluation of speech representations
Fair Voice Biometrics	Demographic imbalance and group fairness
SVeritas	Broad stressor coverage, including compound conditions
InterBias-SV	Additive contrasts and their sampling and metric requirements

Table 1: Evaluation emphasis of related resources. The comparison describes reported analyses, not an exhaustive claim about what their data could support.

Experiment	$\mathrm{IE}$	SD	$\mathrm{NLI}$	$\mathrm{IE}>0$	$n$	$\mathrm{IE}$	SD	$\mathrm{NLI}$	$\mathrm{IE}>0$	$n$
	SV labels					SSL labels
E13 age $\times$ codec	+0.0026	0.0204	1.089	52	84	-0.0063	0.0209	0.973	35	60
E14 emotion $\times$ noise	+0.0088	0.0301	1.549	34	441	-0.0465	0.0540	0.925	22	315
E17 drift $\times$ codec	+0.0024	0.0094	1.137	62	84	+0.0049	0.0198	1.014	57	60

Table 2: Descriptive additive contrasts from available same-corpus terms. Means and SDs are across model–setting combinations;

n

counts those combinations, not independent replicates. EER uses fractional units; “

\mathrm{IE}>0

” is a percentage. SV denotes nominal speaker-trained labels and SSL denotes frozen mean-pooled configurations. E13 compares an age stratum with an overlapping all-age reference; E17’s temporal provenance is unresolved. For E14 SV,

\mathrm{NLI}

averages 198 positive-denominator cells;

n=441

includes all contrasts, including 243 with nonpositive predictions.

	SV labels			SSL labels
Exp.	$\bar{\widehat{\mathrm{EER}}}$	$\max\widehat{\mathrm{EER}}$	$r$	$\bar{\widehat{\mathrm{EER}}}$	$\max\widehat{\mathrm{EER}}$	$r$
E13	0.053	0.125	+0.070	0.254	0.399	+0.012
E14	0.039	0.351	+0.504	0.438	0.670	-0.706
E17	0.048	0.208	-0.220	0.392	0.467	-0.293

Table 3: Additive-prediction summaries.

\bar{\widehat{\mathrm{EER}}}

and

\max\widehat{\mathrm{EER}}

are the mean and maximum prediction;

r

is Pearson correlation between the prediction and

\mathrm{IE}

. These are descriptive diagnostics, not a test that distinguishes saturation from mathematical coupling.

Appendix figures & tables27 assets

Supplementary material from the paper’s appendix.

Appendix

Operator	Settings	Implementation
Telephony codec	G.711, GSM 06.10, AMR-NB, Opus	ffmpeg encode–decode round trip; G.711 $\mu$ -law, GSM and AMR-NB at 8 kHz, Opus at 16 kHz / 12 kbit/s; failures return original audio
Additive noise	Gaussian, environmental, crosstalk; 5/15/25 dB SNR	environmental and crosstalk labels share a RIRS_NOISES directory ( Ko et al., 2017 ) ; missing files trigger Gaussian fallback
Packet loss	0.5%, 2%, 5%, 10%	20 ms frames zeroed independently; no packet-loss concealment or burst-loss model
Speaking rate	$0.75\times$ , $1.0\times$ , $1.25\times$	time-scale perturbation
Wireless codec p	32–990 kbit/s	every condition encoded with AAC at a varying bitrate under labels nominally denoting SBC, aptX and LDAC; measures perceptual-codec bitrate sensitivity, not codec identity
Voice mode p	whisper, shout	signal-level DSP proxy, not recorded phonation-mode speech; EARS contains genuine whispered and shouted recordings, which this operator does not use

Appendix

Table 4: Legacy harness operators (not the producing revisions for all retained rows). Proxy operators are marked

p

. Execution and checkpoint provenance are not verified by the result rows.

T	ID	Factor(s)	Corpus	Enc.	Cond.	Scored	Trials	Use
1	E01	Emotional speech	RAVDESS	12	15	180	330,000	marginal
1	E02	Dysarthric speech	TORGO	12	4	48	153,706	marginal
1	E03	Speaking rate	CommonVoice, EARS	12	18	216	2,159,910	marginal
1	E04	Recorded voice style	EARS	12	9	108	117,036	marginal
1	E05	Codec round trips	not recorded	12	15	180	1,799,550	marginal
1	E06	Packet loss	CommonVoice, EARS	12	18	216	2,159,901	marginal

Appendix

Table 5: Experiment inventory. “Scored” counts model–condition records with a computed EER. “Use” is what the experiment is admitted for:

\dagger

cross-corpus populations: descriptive only;

\ddagger

temporal/age proxy interpretation;

\lx@sectionsign

crosses a population attribute with a stressor, analysed as a subgroup gap;

p

legacy proxy implementation only. E05, E10, and E11 use operator labels rather than corpus identifiers in their dataset fields. All 12 retained encoders have complete coverage of each observed condition grid.

Tier	ID	Factor(s)	Enc.	Cond.	Scored	Trials	EER	minDCF	AUC
1	E01	Emotional speech	12	15	180	330,000	0.172 $\pm$ 0.137	0.617	0.880
1	E02	Dysarthric speech	12	4	48	153,706	0.318 $\pm$ 0.109	0.964	0.737
1	E03	Speaking rate	12	18	216	2,159,910	0.351 $\pm$ 0.132	0.887	0.688
1	E04	Recorded voice style	12	9	108	117,036	0.182 $\pm$ 0.183	0.531	0.850
1	E05	Codec round trips	12	15	180	1,799,550	0.252 $\pm$ 0.161	0.686	0.791
1	E06	Packet loss	12	18	216	2,159,901	0.188 $\pm$ 0.162	0.582	0.853

Appendix

Table 6: Experiment inventory with aggregate scores. EER is the unweighted mean over scored records with its standard deviation across those records.

\dagger

cross-corpus populations, reported descriptively;

\ddagger

temporal/age proxy interpretation.

Encoder	Family	EER	minDCF	AUC	Exp.
CAM++	SV	0.098	0.347	0.933	17
ECAPA-TDNN	SV	0.101	0.360	0.932	17
ECAPA-TDNN-Large	SV	0.102	0.357	0.931	17
Titanet	SV	0.104	0.341	0.928	17
RawNet3	SV	0.113	0.409	0.917	17
RedimNet	SV	0.115	0.381	0.919	17

Appendix

Table 7: Macro-average encoder results. “Exp.” is the number of experiments contributing to that encoder’s macro average.

Condition	Type	EER	SD	minDCF	AUC	Ratio
neutral/neutral	matched	0.083	0.114	0.424	0.951	1.00 $\times$
calm/calm	matched	0.106	0.119	0.378	0.936	1.28 $\times$
surprise/surprise	matched	0.116	0.117	0.501	0.927	1.40 $\times$
disgust/disgust	matched	0.131	0.127	0.513	0.918	1.59 $\times$
neutral $\rightarrow$ calm	mismatched	0.136	0.137	0.527	0.905	1.65 $\times$
happy/happy	matched	0.151	0.128	0.580	0.899	1.84 $\times$

Appendix

Table 8: E01, RAVDESS. Mean EER over available encoders, with the standard deviation across encoders; the last column is the ratio to the neutral/neutral reference. “Matched” conditions use the same emotion for enrolment and test. The SD is between-encoder variability, not a confidence interval. All conditions cover the same 12 model labels. Ratios divide the condition mean by the neutral-reference mean; they do not quantify sampling uncertainty.

Condition	EER (F)	EER (M)	$\|$ gap $\|$	max $\|$ gap $\|$	Enc.
clean (reference)	0.110	0.117	0.0086	0.0352	12
Gaussian, 25 dB	0.139	0.141	0.0191	0.0595	12
Gaussian, 15 dB	0.139	0.146	0.0129	0.0432	12
Gaussian, 5 dB	0.159	0.180	0.0222	0.0450	12
environmental, 25 dB	0.131	0.147	0.0182	0.0811	12
environmental, 15 dB	0.171	0.187	0.0230	0.0523	12

Appendix

Table 9: E16 gender gap on Common Voice. F and M are the recorded subgroup labels; columns give mean EERs over 12 model labels;

|

gap

|

is the mean absolute per-encoder difference, and max

|

gap

|

its largest value over encoders.

Encoder	Clean gap	Mean $\|$ gap $\|$	Ratio range
CAM++	-0.0022	0.0208	-37.7 to +8.2
ECAPA-TDNN	-0.0076	0.0158	-5.2 to +3.8
ECAPA-TDNN-Large	-0.0106	0.0194	-6.3 to +1.9
HuBERT-Large	+0.0036	0.0198	-17.5 to +3.0
RawNet3	-0.0048	0.0164	-6.4 to +6.8
RedimNet	+0.0012	0.0188	-25.5 to +43.5

Appendix

Table 10: Instability of ratio-form bias amplification on E16. The clean gender gap is the denominator of the ratio; “mean

|

gap

|

” is the mean absolute gap over the nine noise conditions. The ratio range is reported to show its sensitivity to a near-zero denominator, not as a reliable amplification estimate.

Exp.	Encoder	Fam.	$\overline{\mathrm{IE}}$	SD	min	max	$\overline{\mathrm{NLI}}$	$n$
E13	CAM++	SV	+0.0028	0.0103	-0.0127	+0.0248	1.226	12
E13	ECAPA-TDNN	SV	+0.0019	0.0141	-0.0154	+0.0218	1.020	12
E13	ECAPA-TDNN-Large	SV	-0.0050	0.0158	-0.0467	+0.0112	0.940	12
E13	HuBERT-Large	SSL	+0.0003	0.0334	-0.0668	+0.0520	1.007	12
E13	RawNet3	SV	-0.0046	0.0178	-0.0491	+0.0240	0.949	12
E13	RedimNet	SV	+0.0087	0.0116	-0.0119	+0.0297	1.356	12

Appendix

Table 11: Interaction effect by encoder and experiment, over the settings for which all four factorial terms are available.

n

is the number of settings.

Exp.	Setting	Obs. (all)	Add. (all)	$\mathrm{IE}$ (SV)	$\mathrm{IE}$ (SSL)
E13	middle amr	0.153	0.149	0.0025	0.0053
E13	middle g711	0.148	0.143	0.0016	0.0089
E13	middle gsm	0.158	0.153	0.0047	0.0054
E13	middle opus	0.134	0.137	-0.0019	-0.0055
E13	older amr	0.141	0.146	0.0117	-0.0265
E13	older g711	0.135	0.140	0.0016	-0.0132

Appendix

Table 12: Per-setting interaction summary. “Obs.” and “Add.” pool all available encoder labels; the final two columns give family-specific mean contrasts.

Encoder A	Encoder B	Cond.	Agree (%)	$\rho$	MAD
CAM++	ECAPA-TDNN-Large	339	20.9	0.993	0.0075
CAM++	Titanet	339	20.6	0.984	0.0126
ECAPA-TDNN-Large	Titanet	339	19.5	0.982	0.0139
ECAPA-TDNN-Large	RedimNet	339	18.0	0.919	0.0184
ECAPA-TDNN	Titanet	339	16.5	0.846	0.0270
CAM++	RedimNet	339	16.2	0.921	0.0187

Appendix

Table 13: The twelve most similar encoder pairs of 66, by exact agreement over co-observed conditions. “Cond.” is the number of co-observed conditions, “Agree” the percentage on which the two EERs are identical,

\rho

the correlation of their EERs, and MAD the mean absolute EER difference.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
ravdess/neutral_neutral	ravdess	12	0.083	0.114	0.424	0.951	120,000
ravdess/calm_calm	ravdess	12	0.106	0.119	0.378	0.936	15,000
ravdess/surprise_surprise	ravdess	12	0.116	0.117	0.501	0.927	15,000
ravdess/disgust_disgust	ravdess	12	0.131	0.127	0.513	0.918	15,000
ravdess/neutral_calm	ravdess	12	0.136	0.137	0.527	0.905	15,000
ravdess/happy_happy	ravdess	12	0.151	0.128	0.580	0.899	15,000

Appendix

Table 14: E01 – Emotional speech: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
TORGO/dysarthria_moderate_matched	torgo	12	0.302	0.117	0.941	0.755	37,404
TORGO/control_matched	torgo	12	0.318	0.120	0.962	0.735	39,689
TORGO/dysarthria_mild_matched	torgo	12	0.326	0.105	0.978	0.730	38,154
TORGO/dysarthria_severe_matched	torgo	12	0.327	0.106	0.977	0.726	38,459

Appendix

Table 15: E02 – Dysarthric speech: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
common_voice/clean	CV	12	0.108	0.118	0.454	0.933	120,000
common_voice/slow_slow	CV	12	0.167	0.077	0.767	0.899	120,000
common_voice/fast_fast	CV	12	0.192	0.070	0.798	0.881	120,000
ears/clean	ears	12	0.244	0.174	0.658	0.796	119,990
common_voice/fast_normal	CV	12	0.336	0.091	0.927	0.718	120,000
common_voice/normal_fast	CV	12	0.337	0.090	0.928	0.718	120,000

Appendix

Table 16: E03 – Speaking rate: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
shout_shout	ears	12	0.148	0.183	0.428	0.870	13,332
modal_modal	ears	12	0.148	0.182	0.424	0.875	13,332
whisper_whisper	ears	12	0.157	0.165	0.477	0.880	13,332
modal_shout	ears	12	0.162	0.189	0.441	0.864	12,840
shout_modal	ears	12	0.171	0.201	0.449	0.853	12,840
whisper_modal	ears	12	0.209	0.194	0.626	0.827	12,840

Appendix

Table 17: E04 – Recorded voice style: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
aac_high_matched	–	12	0.246	0.172	0.669	0.797	119,970
clean	–	12	0.246	0.172	0.660	0.796	119,970
aac_medium_matched	–	12	0.246	0.171	0.671	0.797	119,970
aptx_high_matched	–	12	0.246	0.172	0.662	0.796	119,970
sbc_high_matched	–	12	0.246	0.172	0.669	0.796	119,970
aptx_vs_aac_high	–	12	0.246	0.171	0.671	0.796	119,970

Appendix

Table 18: E05 – Codec round trips: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
common_voice/clean	CV	12	0.108	0.117	0.463	0.933	120,000
common_voice/clean_x_0.5pct	CV	12	0.109	0.118	0.462	0.931	120,000
common_voice/0.5pct_0.5pct	CV	12	0.110	0.118	0.463	0.930	120,000
common_voice/clean_x_2.0pct	CV	12	0.112	0.121	0.473	0.929	120,000
common_voice/2.0pct_2.0pct	CV	12	0.112	0.121	0.474	0.928	120,000
common_voice/5.0pct_5.0pct	CV	12	0.116	0.123	0.482	0.925	120,000

Appendix

Table 19: E06 – Packet loss: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
voxceleb1/gap_0yr	voxceleb1	12	0.168	0.184	0.501	0.862	120,000
voxceleb1/gap_5yr	voxceleb1	12	0.176	0.188	0.542	0.855	120,000
voxceleb1/gap_1yr	voxceleb1	12	0.177	0.190	0.522	0.852	120,000
voxceleb1/gap_3yr	voxceleb1	12	0.180	0.193	0.533	0.848	120,000

Appendix

Table 20: E07 – Temporal drift: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
l2_arctic/level_3_vs_native	L2-ARCTIC/CV	12	0.077	0.103	0.372	0.956	19,944
l2_arctic/level_1_vs_native	L2-ARCTIC/CV	12	0.079	0.105	0.386	0.954	19,908
l2_arctic/level_4_vs_native	L2-ARCTIC/CV	12	0.082	0.117	0.350	0.948	19,968
l2_arctic/l2_all_vs_native	L2-ARCTIC/CV	12	0.084	0.116	0.371	0.951	19,932
l2_arctic/level_2_vs_native	L2-ARCTIC/CV	12	0.088	0.116	0.376	0.944	19,932
l2_arctic/level_5_vs_native	L2-ARCTIC/CV	12	0.091	0.120	0.388	0.944	19,932

Appendix

Table 21: E08 – L2 accent: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
real_real_baseline	–	12	0.239	0.170	0.649	0.804	120,000
voicecraft/cross_utt_spoof	–	12	0.311	0.094	0.781	0.750	113,664
e2_tts/cross_utt_spoof	–	12	0.368	0.050	0.913	0.691	118,104
voicecraft/synth_vs_real	–	12	0.433	0.127	0.904	0.584	113,664
voicecraft/real_vs_synth	–	12	0.436	0.130	0.901	0.581	113,568
e2_tts/synth_vs_real	–	12	0.530	0.080	0.996	0.465	118,104

Appendix

Table 22: E10 – TTS spoofing: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
freevc/spoof_vs_genuine	–	12	0.225	0.096	0.620	0.832	60,000
real_real_baseline	–	12	0.241	0.166	0.700	0.801	60,000
knn_vc/spoof_vs_genuine	–	12	0.357	0.060	0.890	0.690	60,000

Appendix

Table 23: E11 – Voice conversion: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
all__clean	CV	12	0.108	0.119	0.462	0.933	120,000
young__clean	CV	12	0.111	0.117	0.449	0.929	39,996
all__opus	CV	12	0.112	0.118	0.471	0.931	120,000
young__opus	CV	12	0.114	0.116	0.462	0.928	39,996
all__g711	CV	12	0.117	0.111	0.528	0.929	120,000
young__g711	CV	12	0.121	0.111	0.498	0.924	39,996

Appendix

Table 24: E13 – Age

\times

codec: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
neutral__clean__None	ravdess	12	0.054	0.079	0.168	0.972	3,456
calm__gaussian__25	ravdess	12	0.067	0.099	0.236	0.955	3,456
calm__clean__None	ravdess	12	0.072	0.088	0.226	0.964	3,456
neutral__gaussian__25	ravdess	12	0.072	0.107	0.257	0.953	3,456
fearful__clean__None	ravdess	12	0.073	0.092	0.228	0.962	3,456
angry__clean__None	ravdess	12	0.078	0.095	0.260	0.948	3,456

Appendix

Table 25: E14 – Emotion

\times

noise: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
EARS/healthy_clean	ears	12	0.246	0.175	0.663	0.797	119,988
EARS/healthy_opus	ears	12	0.259	0.160	0.679	0.787	29,997
EARS/healthy_amr	ears	12	0.260	0.165	0.702	0.786	29,996
EARS/healthy_gsm	ears	12	0.260	0.163	0.718	0.788	29,996
EARS/healthy_g711	ears	12	0.279	0.155	0.755	0.772	29,998
TORGO/dysarthria_moderate_clean	torgo	12	0.303	0.106	0.932	0.752	37,411

Appendix

Table 26: E15 – Pathology and codec (descriptive): condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
CV/all_clean	CV	12	0.109	0.118	0.446	0.932	60,000
CV/F_clean	CV	12	0.110	0.113	0.432	0.932	60,000
CV/M_clean	CV	12	0.117	0.116	0.466	0.927	60,000
CV/all_gaussian_25dB	CV	12	0.131	0.135	0.453	0.912	13,332
CV/F_environmental_25dB	CV	12	0.131	0.138	0.448	0.910	6,660
CV/all_gaussian_15dB	CV	12	0.137	0.143	0.496	0.900	13,332

Appendix

Table 27: E16 – Gender

\times

noise: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
VoxCeleb1/gap_0_clean	voxceleb1	12	0.168	0.186	0.502	0.862	120,000
VoxCeleb1/gap_0_opus	voxceleb1	12	0.169	0.180	0.496	0.862	30,000
VoxCeleb1/gap_5yr_opus	voxceleb1	12	0.176	0.183	0.523	0.860	9,984
VoxCeleb1/gap_1yr_clean	voxceleb1	12	0.176	0.189	0.516	0.853	39,984
VoxCeleb1/gap_5yr_clean	voxceleb1	12	0.176	0.187	0.516	0.856	39,984
VoxCeleb1/gap_3yr_opus	voxceleb1	12	0.178	0.181	0.496	0.854	9,984

Appendix

Table 28: E17 – Temporal drift

\times

codec: condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
CV_EN/native_gsm	CV	12	0.105	0.117	0.388	0.936	15,000
CV_EN/native_amr	CV	12	0.106	0.119	0.381	0.934	15,000
CV_EN/native_clean	CV	12	0.109	0.119	0.438	0.933	60,000
CV_EN/native_opus	CV	12	0.113	0.120	0.424	0.930	15,000
CV_EN/native_g711	CV	12	0.115	0.111	0.456	0.929	15,000
L2-ARCTIC/accent_gsm	L2-ARCTIC	12	0.175	0.171	0.649	0.859	14,951

Appendix

Table 29: E18 – Accent and codec (descriptive): condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Condition	Corpus	Enc.	EER	SD	minDCF	AUC	Trials
VoxCeleb1/age_middle_clean	voxceleb1	12	0.163	0.180	0.463	0.868	15,000
VoxCeleb1/AC_older_opus	voxceleb1	12	0.163	0.178	0.488	0.869	7,500
VoxCeleb1_3way/older_gap5yr_opus	voxceleb1	12	0.166	0.178	0.491	0.862	3,312
VoxCeleb1/gap0_opus	voxceleb1	12	0.167	0.180	0.471	0.867	9,996
VoxCeleb1/AC_middle_opus	voxceleb1	12	0.167	0.177	0.469	0.866	7,500
VoxCeleb1/age_older_clean	voxceleb1	12	0.167	0.183	0.473	0.865	15,000

Appendix

Table 30: E20 – Age

\times

codec

\times

drift (age proxied): condition-level means over the contributing encoder rows. SD is over contributing encoder rows, not a confidence interval.

Explore similar work

Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages
Jul 1, 2026Pol Buitrago, Javier HernandoAutomatic Speaker Verification Cross-Lingual Consistency
1Barcelona Supercomputing Center (BSC), Spain · 2Universitat Polit`ecnica de Catalunya (UPC), Spain
SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification
Jun 2, 2026Junyi Peng, Oldřich Plchot, Xiao Song +9Automatic Speaker Verification Speaker
Brno University of Technology, Czechia · Peking University, China · Xiaomi, China +3
A Unified Uncertainty-Aware Back-End for Speaker Verification: Scoring, Normalization, and Calibration
Sep 1, 2026Junjie Li, Kong Aik LeeAutomatic Speaker Verification Speaker
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR

Jul 1, 2026, eess.ASJ/K move · Enter open · S save

Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages

Pol Buitrago, Javier Hernando

1Barcelona Supercomputing Center (BSC), Spain · 2Universitat Polit`ecnica de Catalunya (UPC), Spain

Automatic Speaker Verification Cross-Lingual Consistency Multilingual Automatic Speech Recognition Cross-Lingual Transfer Speaker Language Pairs

Open paper AskPDF arXiv

Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different languages. However, standard evaluation protocols confound language mismatch with inter-speaker variability, as evaluation is generally performed with different speakers across languages. In this work, we introduce a bilingual same-speaker evaluation set for five Iberian languages, enabling analysis of cross-lingual SV under constant speaker identity. We apply this setup to a HuBERT-based SV system previously shown to exhibit strong language dependence, and analyze results using the Cross-Lingual Transfer Matrix (CLTM) to study pairwise cross-lingual transfer. Our results show that speaker-related variability accounts for part of the observed degradation, but language mismatch remains the main driver of cross-lingual performance loss. These findings provide a more precise characterization of language dependence in cross-lingual SV.

Abstract

Explore similar work

Disentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages

SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification

A Unified Uncertainty-Aware Back-End for Speaker Verification: Scoring, Normalization, and Calibration