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Junichi Yamagishi

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.SD1
same name
  • Junichi Yamagishi — 34 papers, h 12
  • Junichi Yamagishi — 20 papers, h 7
  • Junichi Yamagishi — 6 papers
  • Junichi Yamagishi — 5 papers
  • Junichi Yamagishi — 4 papers, h 5
  • Junichi Yamagishi — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232025
most citedQuantifying Source Speaker Leakage in One-to-One Voice Conversion

1 citations · 1 across the 3 of their papers we have counts for

collaborators

4 papers

cs.SD2025★ 1 cited

Quantifying Source Speaker Leakage in One-to-One Voice Conversion

Scott Wellington, Xuechen Liu, Junichi Yamagishi

Using a multi-accented corpus of parallel utterances for use with commercial speech devices, we present a case study to show that it is possible to quantify a degree of confidence…

cs.CV2025

Exploring Active Data Selection Strategies for Continuous Training in Deepfake Detection

Yoshihiko Furuhashi, Junichi Yamagishi, Xin Wang +2

In deepfake detection, it is essential to maintain high performance by adjusting the parameters of the detector as new deepfake methods emerge. In this paper, we propose a method t…

cs.CV2024

Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis

Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

This paper investigates the effectiveness of self-supervised pre-trained vision transformers (ViTs) compared to supervised pre-trained ViTs and conventional neural networks (ConvNe…

cs.CV2023

How Close are Other Computer Vision Tasks to Deepfake Detection?

Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.