In automatic speech understanding, the division of continuously running speech into syntactic chunks is a great problem. Syntactic boundaries are often marked by prosodic means. For the training of statistic models for prosodic boundaries large data-bases are necessary. For the German Verbmobil project (automatic speech-to-speech translation), we developed a syntactic-prosodic labeling scheme where two main types of boundaries (major syntactic boundaries and syntactically ambiguous boundaries) and some other special boundaries are labeled for a large Verbmobil spontaneous speech corpus. We compare the results of classifiers (multilayer perceptrons and language models) trained on these syntactic-prosodic boundary labels with classifiers trained on perceptual-prosodic and pure syntactic labels. The main advantage of the rough syntactic-prosodic labels presented in this paper is that large amounts of data could be labeled within a short time. Therefore, the classifiers trained with these labels turned out to be superior (recognition rates of up to 96%).
Cite as: Batliner, A., Kompe, R., Kiessling, A., Niemann, H., Nöth, E. (1996) Syntactic-prosodic labeling of large spontaneous speech data-bases. Proc. 4th International Conference on Spoken Language Processing (ICSLP 1996), 1720-1723, doi: 10.21437/ICSLP.1996-437
@inproceedings{batliner96b_icslp,
author={Anton Batliner and Ralf Kompe and Andreas Kiessling and Heinrich Niemann and Elmar Nöth},
title={{Syntactic-prosodic labeling of large spontaneous speech data-bases}},
year=1996,
booktitle={Proc. 4th International Conference on Spoken Language Processing (ICSLP 1996)},
pages={1720--1723},
doi={10.21437/ICSLP.1996-437}
}