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At least 80% of modern and postmodern poems exhibit neither rhyme nor metrical schemes such as iamb or trochee. However, does this mean that they are free of any rhythmical features?TheUS American research onfree verse prosody claimsthe opposite: Modern poets like Whitman, the Imagists, the Beat poets and contemporary Slam poets have developed a postmetrical idea of prosody, using rhythmical features of everyday language, prose, and musical styles like Jazz or Hip Hop. It has spawned a large and complex variety intheir poetic prosodies
which,however,appearto bemuchharderto quantify and regularize than traditional patterns. In our project, we examinethe largest portal for spoken poetry Lyrikline and analysed and classified such rhythmical patterns by using pattern recognition and classification techniques. We integrate a human-in-the-loop approach in which we interleave manual annotation with computational modelling and data-based analysis. Our results are integrated into the website of Lyrikline. Our follow-up project makes our research results available to a wider audience, in particular to high school-level teaching.
In this work we assess whether there is information in pauses in-between utterances of the same or different speakers that are predictive of the following speaker’s utterance. We present models that connect a person’s visual features before they speak to their upcoming utterance. In our experiments we find that outof-the-box pre-trained models can already reach a better-than-chance performance in correlating video embeddings to utterance embeddings. In contrast, models that attempt to predict the first word after the pause do not outperform a unigram model, indicating that our models do not read lips (based e.g. on co-articulation effects) but rather capture more fundamental aspects of the upcoming utterance.
Recent advances in the development of smart homes have led to the availability of a wide variety of devices providing a high level of convenience via gesture and speech control or fully automated operation. Many smart home appliances also address the aspects of safety and electricity savings by automatically powering themselves off after not being used for a while. However, many devices remain in a typical household that are not themselves "smart", or are not primarily electric (such as heating systems). We address the savings aspect by identifying processes involving the use of multiple devices in the electrical flow data, as captured by a smart meter in a modern household, rather than focusing on a single appliance. Therefore, we introduce a novel approach to usage pattern analysis based on the idea that a pattern of device usages as a result of a resident's 'routine' (such as making breakfast) can be interpreted similarly to a natural language 'sentence'; Natural Language Processing (NLP) algorithms can then be used for interpreting the residents' behavior. We introduce the notion of bag-of-devices (BoD), derived from the bag-of-words model used in document classification. In an experiment, we show how we use this model to infer predictions about the inhabitants from device usage, such as the resident leaving for the day or just to fetch the newspaper.