Long audio alignment for automatic subtitling using different phone-relatedness measures
Abstract
In this work, long audio alignment systems for Spanish and English are presented in an automatic subtitling scenario. Pre-recorded contents are automatically recognized at phoneme level by language-dependent phone decoders. A dynamic-programming alignment algorithm finds matches between the automatically decoded phones and the ones in the phonetic transcription for the content’s script. The accuracy of the alignment algorithm is evaluated when applying three non-binary scoring matrices based on phone confusion-pairs from each phone decoder, on phonological similarity and on human perception errors. Alignment results with the three continuous-score matrices are compared to results with a baseline binary matrix, at word and subtitle levels. The non-binary matrices achieved clearly better results. Matrix samples are given in the project’s website.
BIB_text
author = {Aitor Alvarez, Haritz Arzelus, Pablo Ruiz},
title = {Long audio alignment for automatic subtitling using different phone-relatedness measures},
pages = {6280-6284},
keywds = {
Long audio alignment, phonological similarity matrices, perceptual confusion matrices, automatic subtitling
}
abstract = {
In this work, long audio alignment systems for Spanish and English are presented in an automatic subtitling scenario. Pre-recorded contents are automatically recognized at phoneme level by language-dependent phone decoders. A dynamic-programming alignment algorithm finds matches between the automatically decoded phones and the ones in the phonetic transcription for the content’s script. The accuracy of the alignment algorithm is evaluated when applying three non-binary scoring matrices based on phone confusion-pairs from each phone decoder, on phonological similarity and on human perception errors. Alignment results with the three continuous-score matrices are compared to results with a baseline binary matrix, at word and subtitle levels. The non-binary matrices achieved clearly better results. Matrix samples are given in the project’s website.
}
isi = {1},
date = {2014-05-09},
year = {2014},
}
