---
type: source
id: san-segundo-2026-audio-language-style
title: 'Human perception of audio deepfakes: the role of language and speaking style'
authors:
- Eugenia San Segundo
- Aurora López-Jareño
- Xin Wang
- Junichi Yamagishi
year: 2026
venue: Speech Communication
volume: '185'
pages: '103487'
doi: 10.1016/j.specom.2026.103487
peer_review: peer-reviewed-journal
evidence_tier: B
design: 'Perception experiment: listeners classified voices as natural or synthetic and justified each choice; stimuli varied by language (Spanish, Japanese), speaking style (audiobook, interview) and voice familiarity (familiar, unfamiliar)'
modality:
- audio
stimuli: 80 stimuli, 50% artificial (synthesis system not in abstract)
n_participants: 54
n_note: 28 native Spanish speakers, 26 Japanese speakers
topics:
- human-detection
- audio
- cross-linguistic
- cues
summary: Spanish and Japanese listeners averaged 59.11% when classifying natural vs synthetic speech, doing better on real voices, and relied mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter).
results:
- metric: Average accuracy
  value: 59.11%
  note: better on authentic samples
  source_location: abstract
- metric: Cues used
  value: mainly suprasegmental / extralinguistic
  note: intonation, rhythm, fluency, pauses, speed, breathing, laughter rather than segmental features
  source_location: abstract
- metric: Cross-linguistic
  value: shared cues plus notable differences in how 'humanness' is conceptualised
  note: Japanese vs Spanish listeners
  source_location: abstract
moderators:
- Listener language
- Speaking style
- Voice familiarity
limitations:
- Values not checked against full text
- Small N
- Condition-level accuracies not in abstract
verification:
  status: verified-abstract
  confidence: medium
  checked: 2026-10-18
  via: OpenAlex abstract record for doi:10.1016/j.specom.2026.103487
url: null
pmid: null
pmcid: null
arxiv_id: null
openalex_id: null
oa_url: null
cited_in:
- MR-0001
- MR-0003
updated: '2026-10-18'
---

# Human perception of audio deepfakes: the role of language and speaking style

> **Source record** `san-segundo-2026-audio-language-style` · 2026 · Speech Communication  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: medium)  
> **Bottom line:** Spanish and Japanese listeners averaged 59.11% when classifying natural vs synthetic speech, doing better on real voices, and relied mainly on prosodic and extralinguistic cues (intonation, rhythm, fluency, pauses, breathing, laughter).

## Citation {#citation}

Eugenia San Segundo, Aurora López-Jareño, Xin Wang, Junichi Yamagishi (2026). Human perception of audio deepfakes: the role of language and speaking style. *Speech Communication*, 185, 103487. https://doi.org/10.1016/j.specom.2026.103487

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#san-segundo-2026-audio-language-style](/data/citations.json#san-segundo-2026-audio-language-style)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Perception experiment: listeners classified voices as natural or synthetic and justified each choice; stimuli varied by language (Spanish, Japanese), speaking style (audiobook, interview) and voice familiarity (familiar, unfamiliar) |
| Modality | audio |
| Stimuli | 80 stimuli, 50% artificial (synthesis system not in abstract) |
| Participants (N) | 54 |
| N detail | 28 native Spanish speakers, 26 Japanese speakers |
| Topics | human-detection, audio, cross-linguistic, cues |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Average accuracy | 59.11% | better on authentic samples |
| Cues used | mainly suprasegmental / extralinguistic | intonation, rhythm, fluency, pauses, speed, breathing, laughter rather than segmental features |
| Cross-linguistic | shared cues plus notable differences in how 'humanness' is conceptualised | Japanese vs Spanish listeners |

## Moderators {#moderators}

- Listener language
- Speaking style
- Voice familiarity

## Limitations {#limitations}

- Values not checked against full text
- Small N
- Condition-level accuracies not in abstract

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
- [MR-0003](/metareports/voice-cloning-human-detection.md): Voice Clones and Synthetic Speech: How Well Do People Detect Them?

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | medium |
| Checked on | 2026-10-18 |
| Via | OpenAlex abstract record for doi:10.1016/j.specom.2026.103487 |

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.