Integrating Syntactic and Prosodic Information for the Efficient Detection of Empty Categories

We describe a number of experiments that demonstrate the usefulness of prosodic information for a processing module which parses spoken utterances with a feature-based grammar employing empty categories. We show that by requiring certain prosodic properties from those positions in the input where the presence of an empty category has to be hypothesized, a derivation can be accomplished more efficiently. The approach has been implemented in the machine translation project VERBMOBIL and results in a significant reduction of the work-load for the parser.Comment: To appear in the Proceedings of Coling 1996, Copenhagen. 6 page

Fully exploiting the potential of speech dialog in automotive applications

International audienceToday users are faced with infotainment devices and applications of increasing complexity. The design of easy-to-use and intuitive interfaces becomes a more and more challenging task. Users are usually not aware of the underlying applications and their restrictions when they want to use certain functionalities. Therefore, hierarchical menu structures are difficult to handle especially in situations where eyes and hands are occupied with other tasks, such as driving. For quite a while speech-enabled interfaces have been used to solve this problem since they allow users to control various applications without occupying hands and eyes. However, state-of-the-art multimodal applications often do not exploit the full potential that speech dialog offers simply because this modality is not well integrated with the "traditional" modalities such as graphics and haptics. The resulting speech interfaces do not run smoothly, exhibit plenty of inconsistencies concerning the GUI and are thus more or less tedious to use. Such kind of interfaces result in low acceptance because users do not see the immediate benefit. In this paper we present an approach that develops multimodal interfaces in an integrated way, thus ensuring highly consistent interfaces that closely couple the involved modalities and are thus easier to use

Syntactic-prosodic labeling of large spontaneous speech data-bases

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 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%)