CASE Benchmark

Carrier-Agnostic Speaker Verification Evaluation

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Why this exists: I wanted to build a system that indexes spoken conversations—automatically identifying speakers across Discord, phone calls, and in-person meetings. But I hit a wall: the same person produces different embeddings depending on how I encountered them. Current models degrade up to 19× on real-world audio. Humans don't have this problem—we recognize voices regardless of medium. This benchmark measures that gap. Read the full story →

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Can Your Model Handle Real-World Audio?

State-of-the-art speaker verification models achieve <1% EER on clean benchmarks. But what happens when audio passes through phone codecs, cheap webcams, or is replayed through speakers?

The CASE Benchmark answers this question—and the results are eye-opening.

The Problem

Condition	Typical SOTA Performance
Clean Audio	0.6-1.7% EER ✅
Phone Codec	2-4% EER
Laptop Microphone	0.6-1.8% EER
Room Reverb	5-8% EER
Playback Chain	9-13% EER ❌

That's up to 19× worse performance on realistic conditions.

What is a "Playback Chain"?

The hardest scenario: audio encoded, played through a speaker, and re-recorded:

Voice → [Codec] → [Speaker] → [Room Acoustics] → [Microphone] → Recording

This happens when:

• Voice messages are played back and recorded
• Conference calls with speaker playback
• Smart speaker interactions
• Any voice replay attack scenario

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Quick Start

Installation

pip install case-benchmark

# Install with model support
pip install case-benchmark[speechbrain]  # SpeechBrain ECAPA-TDNN
pip install case-benchmark[all-models]   # All supported models

Download Benchmark Data

case-benchmark download --output-dir ./benchmark_data

Evaluate Your Model

# Using built-in model wrappers
case-benchmark evaluate \
    --model speechbrain \
    --benchmark-dir ./benchmark_data \
    --output-dir ./results

# Or programmatically

from case_benchmark import CASEBenchmark, load_model

benchmark = CASEBenchmark("./benchmark_data")
model = load_model("speechbrain")

results = benchmark.evaluate(model)
results.print_summary()
# Clean EER: 0.56%, Degradation: +2.49%

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Benchmark Results

Leaderboard

Rank	Model	Absolute EER	Degradation	Clean EER
1	WeSpeaker ResNet34	3.01%	+2.43%	0.58%
2	SpeechBrain ECAPA-TDNN	3.05%	+2.49%	0.56%
3	CASE HF v2-512	3.53%	+2.31%	1.22%
4	NeMo TitaNet-L	4.05%	+3.39%	0.66%
5	pyannote Embedding	4.47%	+2.79%	1.68%
6	Resemblyzer	10.49%	+5.65%	4.84%

Key Finding: The CASE HF model achieves the lowest degradation factor (+2.31%), validating its carrier-agnostic design.

Context: VoxCeleb1-O SOTA

For reference, current SOTA on VoxCeleb1-O (clean-clean only):

• ResNet293 + VoxBlink2: 0.17% EER (arXiv:2407.11510)
• ERes2NetV2: 0.61% EER (3D-Speaker)

Our benchmark tests production-ready models that are easily accessible. The models above require specialized training (VoxBlink2 dataset, 100K+ speakers) and post-processing (AS-Norm, QMF) not typically used in deployment.

Category Breakdown (WeSpeaker ResNet34)

Category	Avg EER	vs Clean
Clean	0.58%	baseline
Codec	1.73%	+1.15%
Mic	0.59%	+0.01%
Noise	0.73%	+0.15%
Reverb	5.88%	+5.30%
Playback	8.57%	+7.99%

Key Insight: Playback Chains Remain Challenging

All models show significant degradation on playback scenarios (codec→speaker→room→mic chains), though carrier-aware training reduces this gap substantially.

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Evaluation Protocols

The benchmark includes 24 protocols across 6 categories:

Category	Protocols	Description
Clean	1	Baseline (clean vs clean)
Codec	7	GSM, G.711, Opus, MP3
Mic	7	Webcam, laptop, phone, headset
Noise	5	SNR 5-25 dB
Reverb	1	Simulated room acoustics
Playback	3	Full codec→speaker→room→mic chain

Each protocol has 10,000 trials (5,000 target + 5,000 impostor).

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Metrics

Two metrics together describe a model's carrier robustness:

1. Clean EER (Baseline)

Clean EER = EER on clean_clean protocol

• Measures baseline performance under ideal conditions
• Lower is better (e.g., 0.58% is excellent)

2. Degradation Factor (Robustness)

Degradation = Absolute EER − Clean EER

• Measures robustness: how much performance is lost due to carrier effects
• Lower is better (e.g., +2.31% means minimal degradation)
• Independent of baseline—directly measures carrier susceptibility

A model with low Clean EER and low Degradation is ideal. Some models (like CASE HF) trade baseline performance for better robustness.

Note: An earlier "CASE-Score v1" metric used normalized ratios (EER_degraded / EER_clean), but this can misleadingly reward models with poor baselines. See Metrics for full details.

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Supported Models

Built-in wrappers for popular models:

Model	Install	Status
SpeechBrain ECAPA-TDNN	pip install case-benchmark[speechbrain]	✅ Supported
WeSpeaker ResNet34/CAM++	pip install case-benchmark[wespeaker]	✅ Supported
pyannote embedding	pip install case-benchmark[pyannote]	✅ Supported
NVIDIA NeMo TitaNet	pip install case-benchmark[nemo]	✅ Supported
Resemblyzer	pip install case-benchmark[resemblyzer]	✅ Supported
CASE HF v2-512	pip install case-benchmark[case-hf]	✅ Supported

Custom Models

Implement the EmbeddingModel interface:

from case_benchmark.models.base import EmbeddingModel
import numpy as np
from pathlib import Path

class MyModel(EmbeddingModel):
    def load(self, device: str = "cpu") -> None:
        self.model = load_my_model(device)
        self._loaded = True

    def extract_embedding(self, audio_path: Path) -> np.ndarray:
        audio = load_audio(audio_path)
        return self.model.encode(audio).numpy()

    @property
    def embedding_dim(self) -> int:
        return 192

    @property
    def name(self) -> str:
        return "My Custom Model"

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Data

Source

• VoxCeleb1-O: 40 speakers, ~400 utterances (official test set)
• LibriSpeech test-clean: 40 speakers, ~392 utterances
• Total: 80 speakers across both datasets
• Sample rate: 16kHz mono

Degradations Applied

• Codecs: GSM, G.711 (μ-law, A-law), Opus (6k/12k/24k), MP3
• Microphones: Simulated FIR filters for webcam, laptop, phone, etc.
• Noise: DEMAND corpus at various SNR levels
• Reverb: Real RIRs from OpenSLR-28 + BUT ReverbDB
• Playback: Full codec→speaker→room→mic chain

Avoiding Data Leakage

Important: The benchmark uses different data sources than typical training pipelines to ensure proper train/eval separation.

Component	Benchmark Source	Recommended for Training
Noise	DEMAND	MUSAN ✓
Reverb	OpenSLR-28 + BUT ReverbDB (real RIRs)	pyroomacoustics or OpenSLR-26 (simulated) ✓

If you train with MUSAN noise and pyroomacoustics/OpenSLR-26 RIRs, your training data is properly separated from the benchmark. See docs/methodology.md for details.

Download

# From HuggingFace
case-benchmark download --output-dir ./benchmark_data

# Or using huggingface_hub directly
from huggingface_hub import snapshot_download
snapshot_download("bigstorm/case-benchmark", local_dir="./benchmark_data")

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Documentation

• Why This Exists - The problem this benchmark is trying to solve
• Methodology - Benchmark design and technical approach
• Protocols - Detailed protocol descriptions
• Metrics - EER, Degradation Factor, and how to compare models
• Findings - Key results and analysis
• Submission Guide - How to submit to leaderboard

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Citation

If you use the CASE Benchmark in your research, please cite:

@misc{gitter2026case,
  title={CASE Benchmark: Carrier-Agnostic Speaker Verification Evaluation},
  author={Gitter, Ben},
  year={2026},
  howpublished={\url{https://github.com/gittb/case-benchmark}}
}

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License

• Code: MIT License
• Data: CC BY-NC 4.0 (non-commercial research only) (Contact Ben Gitter for a commerical license)

The benchmark audio is derived from VoxCeleb and LibriSpeech, which have their own license terms.

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Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

• Report issues on GitHub Issues
• Submit model results via Pull Request

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Acknowledgments

• VoxCeleb for source audio data (VoxCeleb1-O test set)
• LibriSpeech for source audio data (test-clean subset)
• DEMAND for noise samples used in the benchmark
• OpenSLR-28 and BUT ReverbDB for real room impulse responses