{"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/a-corpus-of-neutral-voice-speech-in-brazilian","title":"A Corpus of Neutral Voice Speech in Brazilian Portuguese","arxiv_id":null,"date":"2021-05-21","proceeding":"International Conference on Computational Processing of the Portuguese Language 2021 5","authors":["Pedro H. L. Leite","Edmundo Hoyle","Álvaro Antelo","Luiz F. Kruszielski","Luiz W. P. Biscainho"],"abstract":"This work presents a new database containing high sampling rate recordings of a single male speaker reading sentences in Brazilian Portuguese with neutral voice, along with the corresponding text corpus. Intended for synthesis and other speech-oriented applications, the dataset contains text scripts extracted from a popular Brazilian news TV program, read out loud by a trained individual in a controlled environment, resulting in roughly 20 h of audio data. The text was normalized in the recording process and special textual occurrences (e.g. acronyms, numbers, foreign names etc.) were replaced by their phonetic translation to a readable text in Portuguese. There are no noticeable accidental sounds and background noise has been kept to a minimum in all audio samples. To illustrate the potential benefits of having this data available, text-to-speech experiments were conducted using state-of-the-art models for speech synthesis (Tacotron 2 and Waveglow). As a result, we obtained intelligible and natural sounding voices from as few as 8 min of audio samples coming from an unseen target speaker, after having trained over our data; moreover, by increasing the target recording time to 75 min, we have noticeably improved accuracy in pronunciation.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-98305-5_32","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-030-98305-5_32","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":"text-to-speech-1","task_name":"text-to-speech"}],"methods":[],"datasets_introduced":[{"slug":"gneutralspeech-male","name":"GneutralSpeech Male","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}