Fakultät Informatik und Mathematik
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Telling stories is different from reading out text: a speakerreponds to the listener’s feedback and incorporates thisinto the ongoing talk. However, current computer sys-tems are unable to do this and instead just non-attentivelyread out a text, disregarding all feedback (or the absencethereof). I propose and discuss an idea for a small researchproject and a plan for how an attentive listening storytellercan be built.
We present a component for incremental speech synthesis (iSS) and a set of applications that demonstrate its capabilities. This component can be used to increase the responsivity and naturalness of spoken interactive systems. While iSS can show its full strength in systems that generate output incrementally, we also discuss how even otherwise unchanged systems may profit from its capabilities.
Participants in a conversation are normally receptive to their surroundings and their interlocutors, even while they are speaking and can, if necessary, adapt their ongoing utterance. Typical dialogue systems are not receptive and cannot adapt while uttering. We present combin-able components for incremental natural lan-guage generation and incremental speech syn-thesis and demonstrate the flexibility they can achieve with an example system that adapts to a listener's acoustic understanding problems by pausing, repeating and possibly rephrasing problematic parts of an utterance. In an evaluation, this system was rated as significantly more natural than two systems representing the current state of the art that either ignore the interrupting event or just pause; it also has a lower response time.
Generating Situated Assisting Utterances to Facilitate Tactile-Map Understanding: A Prototype System
(2012)
Tactile maps are important substitutes for visual maps for blind and visually impaired people and the efficiency of tactile-map reading can largely be improved by giving assisting utterances that make use of spatial language. In this paper, we elaborate earlier ideas for a system that generates such utterances and present a prototype implementation based on a semantic conceptualization of the movements that the map user performs. A worked example shows the plausibility of the solution and the output that the prototype generates given input derived from experimental data.
If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4% perplexity benefit on the Switchboard corpus, and up to a 1.0% relative reduction in word error rate (0.3% absolute) on the Verbmobil II corpus.
We present a model of semantic processing of spoken language that (a) is robust against ill-formed input, such as can be expected from automatic speech recognisers, (b) respects both syntactic and pragmatic constraints in the computation of most likely interpretations, (c) uses a principled, expressive semantic representation formalism (RMRS) with a well-defined model theory, and (d) works continuously (producing meaning representations on a word-by-word basis, rather than only for full utterances) and incrementally (computing only the additional contribution by the new word, rather than re-computing for the whole utterance-so-far). We show that the joint satisfaction of syntactic and pragmatic constraints improves the performance of the NLU component (around 10 % absolute, over a syntax-only baseline).
Incremental speech synthesis (iSS) accepts input and produces output in consecutive chunks that only together result in a full utterance. Systems that use iSS thus have the ability to adapt their utterances while they are ongoing. Having available less than the full utterance to plan the acoustic realisation has downsides, however, as global optimisation is not possible anymore. In this paper we present a strategy for incrementalizing the symbolic pre-processing component of speech synthesis and assess the influence of a reduction in "lookahead", i. e. in knowledge about the rest of the utterance, on prosodic quality. We found that high quality incremental output can be achieved even with a lookahead of slightly less than one phrase, allowing for timely system reaction.
Purpose:
The authors present a stochastic framework for radiotherapy patient positioning directly utilizing radiographic projections. This framework is developed to be robust against anatomical nonrigid deformations and to cope with challenging imaging scenarios, involving only a few cone beam CT projections from short arcs.
Methods:
Specifically, a Bayesian estimator (BE) is explicitly derived for the given scanning geometry. This estimator is compared to reference methods such as chamfer matching (CM) and the minimization of the median absolute error adapted as tools of robust image processing and statistics. In order to show the performance of the stochastic short-arc patient positioning method, a CIRS IMRT thorax phantom study is presented with movable markers and the utilization of an Elekta Synergy® XVI system. Furthermore, a clinical prostate CBCT scan of a Varian® On-Board Imager® system is utilized to investigate the robustness of the method for large variations of image quality (anterior-posterior vs lateral views).
Results:
The results show that the BE shifts reduce the initial setup error of up to 3 cm down to 3 mm at maximum for an imaging arc as short as 10° while CM achieves residual errors of 7 mm at maximum only for arcs longer than 40°. Furthermore, the BE can compensate robustly for low image qualities using several low quality projections simultaneously.
Conclusions:
In conclusion, an estimation method for marker-based patient positioning for short imaging arcs is presented and shown to be robust and accurate for deformable anatomies.