PunkProse [software]
Punctuation marks support understandability and readability in written language. In spoken language, punctuation of the transcribed speech is influenced by two phenomena: (1) syntax and (2) prosody. We present a software architecture that makes it possible to train punctuation restoration models fro...
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| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::4113fafee598ad947ca7ed37552f18c9 |
| Acceso en línea: | https://doi.org/10.34810/DATA484 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer and Information Science Speech transcription Recurrent neural networks Prosody Punctuation generation |
| Sumario: | Punctuation marks support understandability and readability in written language. In spoken language, punctuation of the transcribed speech is influenced by two phenomena: (1) syntax and (2) prosody. We present a software architecture that makes it possible to train punctuation restoration models from any combination of lexical, morphosyntactic, prosodic and acoustic features. Architecture is language independent and feeds on word-segmented data. A dataset compiled from English TED talks is given in http://hdl.handle.net/10230/33981 |
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