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arxiv: 2409.08711 · v2 · pith:YMBMRENX · submitted 2024-09-13 · eess.AS · cs.AI

Text-To-Speech Synthesis In The Wild

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classification eess.AS cs.AI
keywords datadatasetselectiontext-to-speechtitw-easytitw-hardtrainingutmos
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Traditional Text-to-Speech (TTS) systems rely on studio-quality speech recorded in controlled settings.a Recently, an effort known as noisy-TTS training has emerged, aiming to utilize in-the-wild data. However, the lack of dedicated datasets has been a significant limitation. We introduce the TTS In the Wild (TITW) dataset, which is publicly available, created through a fully automated pipeline applied to the VoxCeleb1 dataset. It comprises two training sets: TITW-Hard, derived from the transcription, segmentation, and selection of raw VoxCeleb1 data, and TITW-Easy, which incorporates additional enhancement and data selection based on DNSMOS. State-of-the-art TTS models achieve over 3.0 UTMOS score with TITW-Easy, while TITW-Hard remains difficult showing UTMOS below 2.8.

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

  1. Natural Yet Challenging to Detect: Robust In-the-Wild TTS through EMA and Dual-Scoring Prompt Selection -- Submission for WildSpoof 2026 TTS Track

    eess.AS 2026-05 unverdicted novelty 3.0

    F5-TTS-DPS integrates EMA and dual-scoring prompt selection into F5-TTS to produce in-the-wild TTS that achieves the best a-DCF scores (0.1582, 0.5233, 0.2562) on three SASV systems in the WildSpoof challenge.