Speech Deepfake Detection Eval

---
name: speech-deepfake-detection-eval
description: Evaluates a model's ability to distinguish between authentic human speech and synthetically generated or manipulated speech (deepfakes), with a specific focus on robustness against expressive and emotional synthesis attacks. Use when the user wants to benchmark on LibriSpeech, ASVspoof 2019 LA, ASVspoof 2021 LA, ASVspoof 2024, EmoFake, EmoSpoof-TTS, or asks about evaluating this task. Reports EER (Equal Error Rate).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.13229
  bibtex_key: [REDACTED]
  confidence: high
---

# speech-deepfake-detection-eval

> ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks — Mahapatra et al. (2026) (arXiv:2604.13229, 2026)

## What this evaluates

Evaluates a model's ability to distinguish between authentic human speech and synthetically generated or manipulated speech (deepfakes), with a specific focus on robustness against expressive and emotional synthesis attacks.

## Datasets

- **LibriSpeech** — total ?; splits: train (-1), dev (-1)
- **ASVspoof 2019 LA** — total ?; splits: train (-1), dev (-1), eval (-1)
- **ASVspoof 2021 LA** — total ?; splits: eval (-1)
- **ASVspoof 2024** — total ?; splits: train (-1), dev (-1), eval (-1)
- **EmoFake** — total ?; splits: eval (-1)
- **EmoSpoof-TTS** — total ?; splits: eval (-1)

## Metrics

- `EER (Equal Error Rate)` **(primary)** — range: percent
  - Standard spoof detection metric; not explicitly named in this section but universally used for ASVspoof and EmoFake benchmarks. It is the operating point where the false acceptance rate equals the false rejection rate.
- `validation accuracy` — range: percent
  - Percentage of correctly classified 4-second audio segments on the development set, used for model selection during training.

## Input / output format

**Input**: Fixed 4-second audio segments containing either bona fide or spoofed speech.

**Output**: Binary classification prediction (bona fide vs. spoofed) or spoof probability score.

## Scoring recipe

```python
def compute_eer(scores, labels):
    # scores: model output probabilities for spoof class
    # labels: ground truth (1=spoof, 0=bona fide)
    far_list, frr_list = [], []
    for threshold in np.linspace(0, 1, 1000):
        far = sum(1 for s, l in zip(scores, labels) if s >= threshold and l == 0) / max(sum(1 for l in labels if l == 0), 1)
        frr = sum(1 for s, l in zip(scores, labels) if s < threshold and l == 1) / max(sum(1 for l in labels if l == 1), 1)
        far_list.append(far)
        frr_list.append(frr)
    # Find threshold where FAR ≈ FRR
    eer = np.interp(0.5, far_list, frr_list)
    return eer * 100
```

## Common pitfalls

- Using the development set for final metric reporting instead of the official evaluation set.
- Training baselines inconsistently (e.g., using official pretrained checkpoints for ASVspoof 2019 but training from scratch for ASVspoof 2024).
- Ignoring the official train/dev/eval splits defined by each benchmark organization.

## Evidence (verbatim from paper)

> For traditional benchmarks, we evaluate on ASVspoof 2019 LA and ASVspoof 2021 LA. For emotional and expressive evaluation, we use EmoFake, EmoSpoof-TTS (abbreviated as EmoSpoof in Tables), and ASVspoof 2024 Track 1, which includes modern expressive synthesis systems. All experiments follow the official train/dev/eval splits of each dataset.

## Citation

```bibtex
@misc{mahapatra2026prosdd,
  title={ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks},
  author={Mahapatra et al. (2026)},
  year={2026},
  note={arXiv:2604.13229}
}
```

- arXiv: 2604.13229