PhiladelphiaSYNTACTIC { PROSODIC LABELING OF LARGE SPONTANEOUSSPEECH DATA {

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 databases are necessary. For the German Verb-mobil 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 classiiers (multi-layer perceptrons and language models) trained on these syntactic{prosodic boundary labels with classiiers 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 classiiers trained with these labels turned out to be superior (recognition rates of up to 96%).