Pith. sign in

REVIEW 2 cited by

Towards Naturalistic Voice Conversion: NaturalVoices Dataset with an Automatic Processing Pipeline

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 2406.04494 v1 pith:CJ2RWT5R submitted 2024-06-06 eess.AS

Ali N. Salman , Zongyang Du , Shreeram Suresh Chandra , Ismail Rasim Ulgen , Carlos Busso , Berrak Sisman This is my paper · ORCID
classification eess.AS
keywords speechdatasetnaturalnaturalvoicespipelinedataconversionexpressive
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Voice conversion (VC) research traditionally depends on scripted or acted speech, which lacks the natural spontaneity of real-life conversations. While natural speech data is limited for VC, our study focuses on filling in this gap. We introduce a novel data-sourcing pipeline that makes the release of a natural speech dataset for VC, named NaturalVoices. The pipeline extracts rich information in speech such as emotion and signal-to-noise ratio (SNR) from raw podcast data, utilizing recent deep learning methods and providing flexibility and ease of use. NaturalVoices marks a large-scale, spontaneous, expressive, and emotional speech dataset, comprising over 3,800 hours speech sourced from the original podcasts in the MSP-Podcast dataset. Objective and subjective evaluations demonstrate the effectiveness of using our pipeline for providing natural and expressive data for VC, suggesting the potential of NaturalVoices for broader speech generation tasks.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks

    eess.AS 2026-08 conditional novelty 7.0 of 10

    A 260-hour emotional deepfake benchmark spanning 21 attack systems shows state-of-the-art speech deepfake detectors degrade badly on emotionally expressive and LALM-based spoofing.

  2. Training-Free Multi-Step Audio Source Separation

    cs.SD 2025-05 conditional novelty 6.0 of 10

    Iteratively remixing and re-separating the input mixture, with the best blend chosen by a quality metric, improves pretrained one-step audio separation models without any retraining.

Pith tools