Pith. sign in

REVIEW 1 cited by

MSceneSpeech: A Multi-Scene Speech Dataset For Expressive Speech Synthesis

Not yet reviewed by Pith; the record is open.

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 2407.14006 v1 pith:SNJNDH4B submitted 2024-07-19 eess.AS cs.SD

classification eess.AScs.SD
keywords speechmscenespeechdatasetsynthesisaudiobaselineexpressivemultiple
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We introduce an open source high-quality Mandarin TTS dataset MSceneSpeech (Multiple Scene Speech Dataset), which is intended to provide resources for expressive speech synthesis. MSceneSpeech comprises numerous audio recordings and texts performed and recorded according to daily life scenarios. Each scenario includes multiple speakers and a diverse range of prosodic styles, making it suitable for speech synthesis that entails multi-speaker style and prosody modeling. We have established a robust baseline, through the prompting mechanism, that can effectively synthesize speech characterized by both user-specific timbre and scene-specific prosody with arbitrary text input. The open source MSceneSpeech Dataset and audio samples of our baseline are available at https://speechai-demo.github.io/MSceneSpeech/.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching

    eess.AS 2025-06 conditional novelty 5.0 of 10

    R-VC performs zero-shot voice conversion in two sampling steps while transferring the target speaker's rhythm, matching or exceeding prior systems in naturalness and intelligibility.

Pith tools