Enhancements in statistical spoken language translation by de-normalization of ASR results
Abstract
Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speech recognition output and for aiding downstream language processing. This article focuses on the automatic sentence segmentation of speech and improving MT results. We explore the problem of identifying sentence boundaries in the transcriptions produced by automatic speech recognition systems in the Polish language. We also experiment with reverse normalization of the recognized speech samples.
Keywords
Cite
@article{arxiv.1511.09392,
title = {Enhancements in statistical spoken language translation by de-normalization of ASR results},
author = {Agnieszka Wołk and Krzysztof Wołk and Krzysztof Marasek},
journal= {arXiv preprint arXiv:1511.09392},
year = {2015}
}
Comments
International Academy Publishing. arXiv admin note: text overlap with arXiv:1510.04500