{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neutral-tts-female-voice-corpus-in-brazilian","title":"Neutral TTS Female Voice Corpus in Brazilian Portuguese","arxiv_id":null,"date":"2023-10-08","proceeding":"XLI Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2023) 2023 10","authors":["Pedro H. L. Leite","Edmundo Hoyle","Álvaro Antelo","Luiz F. Kruszielski","Luiz W. P. Biscainho"],"abstract":"This paper introduces a new dataset designed to address the limitations in high-quality, diverse and representative datasets for training text-to-speech (TTS) models, specifically for female voices in Brazilian Portuguese. The dataset features a female voice recorded in a professional and controlled environment with neutral emotion and comprises more than 20 hours of recordings. The goal is to facilitate transfer learning and enable the development of more natural-sounding, high-quality, and gender-balanced TTS systems. Alongside the dataset, gender-aware voice transfer experiments are performed to understand the impact of utilizing gender-specific pretrained models for speech synthesis. The results obtained show that same-gender voice transfer yields better speech similarity and intelligibility when compared to cross-gender transfer, emphasizing the importance of gender-aware training procedures and highlighting the need for balanced gender data.","url_abs":"https://biblioteca.sbrt.org.br/articles/4464","url_pdf":"https://biblioteca.sbrt.org.br/articles/4464","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"text-to-speech","task_name":"Text to Speech"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"text-to-speech-1","task_name":"text-to-speech"}],"methods":[],"datasets_introduced":[{"slug":"gneutralspeech-female","name":"GneutralSpeech Female","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}