Autor:	Ward, Nigel G., Avila, Jonathan E., Rivas, Emilia, Marco, Divette
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	To support machine learning of cross-language prosodic mappings and other ways to improve speech-to-speech translation, we present a protocol for collecting closely matched pairs of utterances across languages, a description of the resulting data collection and its public release, and some observations and musings. This report is intended for: people using this corpus, people extending this corpus, and people designing similar collections of bilingual dialog data. Comment: Version 2
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.11584 Zobrazit plný text záznamu View this record from Arxiv