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acessibilidade

http://hdl.handle.net/10071/22063
acessibilidade
Title: Recovering capitalization and punctuation marks for automatic speech recognition: case study for Portuguese broadcast news
Authors: Batista, F.
Caseiro, D.
Mamede, N.
Trancoso, I.
Keywords: Rich transcription
Punctuation recovery
Sentence boundary detection
Capitalization
Truecasing
Maximum entropy
Language modeling
Weighted finite state transducers
Issue Date: 2008
Publisher: Elsevier
Abstract: The following material presents a study about recovering punctuation marks, and capitalization information from European Portuguese broadcast news speech transcriptions. Different approaches were tested for capitalization, both generative and discriminative, using: finite state transducers automatically built from language models; and maximum entropy models. Several resources were used, including lexica, written newspaper corpora and speech transcriptions. Finite state transducers produced the best results for written newspaper corpora, but the maximum entropy approach also proved to be a good choice, suitable for the capitalization of speech transcriptions, and allowing straightforward on-the-fly capitalization. Evaluation results are presented both for written newspaper corpora and for broadcast news speech transcriptions. The frequency of each punctuation mark in BN speech transcriptions was analyzed for three different languages: English, Spanish and Portuguese. The punctuation task was performed using a maximum entropy modeling approach, which combines different types of information both lexical and acoustic. The contribution of each feature was analyzed individually and separated results for each focus condition are given, making it possible to analyze the performance differences between planned and spontaneous speech. All results were evaluated on speech transcriptions of a Portuguese broadcast news corpus. The benefits of enriching speech recognition with punctuation and capitalization are shown in an example, illustrating the effects of described experiments into spoken texts.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/22063
DOI: 10.1016/j.specom.2008.05.008
ISSN: 0167-6393
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-16009
Accession number: WOS:000260702200006
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica

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