4th International Conference on Spoken Language Processing
Philadelphia, PA, USA
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%).
Bibliographic reference. Batliner, Anton / Kompe, Ralf / Kiessling, Andreas / Niemann, Heinrich / Nöth, Elmar (1996): "Syntactic-prosodic labeling of large spontaneous speech data-bases", In ICSLP-1996, 1720-1723.