Pith. sign in

REVIEW

The Database and Benchmark for the Source Speaker Tracing Challenge 2024

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.04951 v2 pith:KGQCG3NF submitted 2024-06-07 eess.AS

Ze Li , Yuke Lin , Tian Yao , Hongbin Suo , Pengyuan Zhang , Yanzhen Ren , Zexin Cai , Hiromitsu Nishizaki
show 1 more author
Ming Li
This is my paper
classification eess.AS
keywords speakersystemstaskdatabasesourcesstcbenchmarkchallenge
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Voice conversion (VC) systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification (SV) systems. However, ongoing studies on source speaker verification (SSV) are hindered by limited data availability and methodological constraints. This paper presents the Source Speaker Tracking Challenge (SSTC) on STL 2024, which aims to fill the gap in the database and benchmark for the SSV task. In this study, we generate a large-scale converted speech database with 16 common VC methods and train a batch of baseline systems based on the MFA-Conformer architecture. In addition, we introduced a related task called conversion method recognition, with the aim of assisting the SSV task. We expect SSTC to be a platform for advancing the development of the SSV task and provide further insights into the performance and limitations of current SV systems against VC attacks. Further details about SSTC can be found in https://sstc-challenge.github.io/.

Discussion (0). Sign in to comment.

Pith tools