Pith. sign in

REVIEW 1 cited by

SRC4VC: Smartphone-Recorded Corpus for Voice Conversion Benchmark

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 2406.07254 v1 pith:X7AVIAKD submitted 2024-06-11 cs.SD eess.AS

SRC4VC: Smartphone-Recorded Corpus for Voice Conversion Benchmark

Yuki Saito , Takuto Igarashi , Kentaro Seki , Shinnosuke Takamichi , Ryuichi Yamamoto , Kentaro Tachibana , Hiroshi Saruwatari This is my paper
classification cs.SD eess.AS
keywords speechsamplessrc4vcvoicebenchmarkconversioncorpushigh-quality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

We present SRC4VC, a new corpus containing 11 hours of speech recorded on smartphones by 100 Japanese speakers. Although high-quality multi-speaker corpora can advance voice conversion (VC) technologies, they are not always suitable for testing VC when low-quality speech recording is given as the input. To this end, we first asked 100 crowdworkers to record their voice samples using smartphones. Then, we annotated the recorded samples with speaker-wise recording-quality scores and utterance-wise perceived emotion labels. We also benchmark SRC4VC on any-to-any VC, in which we trained a multi-speaker VC model on high-quality speech and used the SRC4VC speakers' voice samples as the source in VC. The results show that the recording quality mismatch between the training and evaluation data significantly degrades the VC performance, which can be improved by applying speech enhancement to the low-quality source speech samples.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Alethia: A Foundational Encoder for Voice Deepfakes

    cs.SD 2026-04 unverdicted novelty 6.0

    Alethia is a pretrained audio encoder using continuous embedding prediction and generative flow-matching reconstruction that outperforms existing speech foundation models on voice deepfake tasks with better robustness...