---
id: yamagishi26_interspeech
title: Countermeasures Against Misuse of Speech Generative AI
authors:
  - Junichi Yamagishi
year: 2026
isca_url: https://www.isca-archive.org/interspeech_2026/yamagishi26_interspeech.html
pdf_url: https://www.isca-archive.org/interspeech_2026/yamagishi26_interspeech.pdf
session: "Keynote4 - Junichi Yamagishi: Countermeasures Against Misuse of Speech
  Generative AI"
topics:
  - audio-deepfake
category: deepfake-security
institutions:
  - National Institute of Informatics
code:
  url: ""
  license: ""
open_to_collaboration: false
wiki_frontmatter:
  id: yamagishi26_interspeech
  category: deepfake-security
  institutions:
    - National Institute of Informatics
  updated: 2026-09-28
  confidence: abstract-only
  source: https://www.isca-archive.org/interspeech_2026/yamagishi26_interspeech.html
wiki_url: https://interspeech-2026-wiki.vercel.app/papers/yamagishi26_interspeech/
markdown_url: https://interspeech-2026-wiki.vercel.app/papers/yamagishi26_interspeech/markdown.md
---

# Countermeasures Against Misuse of Speech Generative AI

[ISCA page](https://www.isca-archive.org/interspeech_2026/yamagishi26_interspeech.html) *(keynote — no PDF in the archive)*

**Category:** `deepfake-security`

**TL;DR** — Keynote surveying how the speech community can defend against misuse of generative speech AI, combining passive defenses (deepfake detection) with proactive ones, toward a comprehensive security ecosystem.

## Problem

Speech generative AI now produces human-quality content, which enables misinformation and impersonation fraud at scale. Sustainable AI research therefore needs countermeasure research and real-world adoption, not just ever-better generators.

## Method

The talk organizes speech-side defenses into two families: passive defense centered on deepfake detection (analyzing anomalies in media features to judge authenticity across many generator tools), positioned as a layer complementary to provenance certification (C2PA) and audio watermarking; and proactive defenses discussed as part of building an end-to-end security ecosystem.

## Results

Keynote/overview — presents research outcomes across passive and proactive defense rather than a single benchmark result.

## Code

None released (as of this page's `updated` date). If you are an author with a repo, please claim this entry — see CONTRIBUTING.md.

## Applications

Anyone deploying speech generation responsibly, and teams building deepfake detection, watermarking, or provenance pipelines.

## Institutions / 機構

National Institute of Informatics

## Related

- [FreqGuard: Leveraging Frequency-Domain Feature Priors for Universal Proactive Voice Defense](wang26ca_interspeech.md) — same problem · relatedness 2.1/3
- [A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography](ozer26_interspeech.md) — same problem · relatedness 2.1/3
- [NaVo: Natural Voice Protection against Voice Cloning Attacks via Generative Universal Adversarial Audio](park26g_interspeech.md) — same problem · relatedness 2.1/3
- [DeepFense: A Unified, Modular, and Extensible Framework for Robust Audio Deepfake Detection](kheir26_interspeech.md) — same problem · relatedness 2.0/3
- [Phoneme-Aware Mamba Watermark: An Active Defense System Against Purified Speech Deepfakes](shao26_interspeech.md) — same problem · relatedness 2.0/3

<sub>All 950k paper pairs scored by TypeSafe Jev (`scripts/related/`); relatedness 0 = unrelated … 3 = directly comparable.</sub>