@inproceedings{Baumann, author = {Baumann, Timo}, title = {Highly Responsive Vocal Interaction through Incremental Processing}, series = {Vocal Interactivity in-and-between Humans, Animals and Robots (VIHAR), October 30-4, 2016}, volume = {6}, booktitle = {Vocal Interactivity in-and-between Humans, Animals and Robots (VIHAR), October 30-4, 2016}, number = {10}, editor = {Moore, Roger K. and Thill, Serge and Marxer, Ricard}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, doi = {10.4230/DagRep.6.10.154}, pages = {154 -- 194}, abstract = {This seminar was held in late 2016 and brought together, for the first time, researchers studying vocal interaction in a variety of different domains covering communications between all possible combinations of humans, animals, and robots. While each of these sub-domains has extensive histories of research progress, there is much potential for cross-fertilisation that currently remains underexplored. This seminar aimed at bridging this gap. In this report, we present the nascent research field of VIHAR and the major outputs from our seminar in the form of prioritised open research questions, abstracts from stimulus talks given by prominent researchers in their respective fields, and open problem statements by all participants.}, language = {en} } @inproceedings{MeyerSickendiekHusseinBaumann, author = {Meyer-Sickendiek, Burkhard and Hussein, Hussein and Baumann, Timo}, title = {Rhythmicalizer: Data Analysis for the Identification of Rhythmic Patterns in Readout Poetry}, series = {INFORMATIK 2017, Tagung vom 25.- 29. September 2017 in Chemnitz}, booktitle = {INFORMATIK 2017, Tagung vom 25.- 29. September 2017 in Chemnitz}, editor = {Eibl, Maximilian and Gaedke, Martin}, publisher = {Gesellschaft f{\"u}r Informatik}, address = {Bonn}, isbn = {978-3-88579-669-5}, doi = {10.18420/in2017_218}, pages = {2189 -- 2200}, abstract = {The most important development in modern and postmodern poetry is the replacement of traditional meter by new rhythmical patterns. Ever since Walt Whitman's Leaves of Grass (1855), modern (nineteenth-to twenty-first-century) poets have been searching for novel forms of prosody, accent, rhythm, and intonation. Along with the rejection of older metrical units such as the iamb or trochee, a structure of lyrical language was developed that renounced traditional forms like rhyme and meter. This development is subsumed under the term free verse prosody. Our project will test this theory by applying machine learning or deep learning techniques to a corpus of modern and postmodern poems as read aloud by the original authors. To this end, we examine "lyrikline", the most famous online portal for spoken poetry. First, about 17 different patterns being characteristic for the lyrikline-poems have been identified by the philological scholar of this project. This identification was based on a certain philological method including three different steps: a) grammetrical ranking; b) rhythmic phrasing; and c) mapping rubato and prosodic phrasing. In this paper we will show how to combine this philological and a digital analysis by using the prosody detection available in speech processing technology. In order to analyse the data, we want to use different tools for the following tasks: PoS-tagging, alignment, intonation, phrases and pauses, and tempo. We also analyzed the lyrikline-data by identifying the occurrence of the mentioned patterns. This analysis is a first step towards an automatic classification based on machine learning or deep learning techniques.}, language = {en} } @incollection{BaumannKenningtonHoughetal., author = {Baumann, Timo and Kennington, Casey and Hough, Julian and Schlangen, David}, title = {Recognising Conversational Speech: What an Incremental ASR Should Do for a Dialogue System and How to Get There}, series = {Dialogues with social robots: Enablements analyses and evaluation}, booktitle = {Dialogues with social robots: Enablements analyses and evaluation}, editor = {Jokinen, Kristiina and Wilcock, Graham}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-10-2584-6}, doi = {10.1007/978-981-10-2585-3_35}, pages = {421 -- 432}, abstract = {Automatic speech recognition (ASR) is not only becoming increasingly accurate, but also increasingly adapted for producing timely, incremental output. However, overall accuracy and timeliness alone are insufficient when it comes to interactive dialogue systems which require stability in the output and responsivity to the utterance as it is unfolding. Furthermore, for a dialogue system to deal with phenomena such as disfluencies, to achieve deep understanding of user utterances these should be preserved or marked up for use by downstream components, such as language understanding, rather than be filtered out. Similarly, word timing can be informative for analyzing deictic expressions in a situated environment and should be available for analysis. Here we investigate the overall accuracy and incremental performance of three widely used systems and discuss their suitability for the aforementioned perspectives. From the differing performance along these measures we provide a picture of the requirements for incremental ASR in dialogue systems and describe freely available tools for using and evaluating incremental ASR.}, language = {en} } @inproceedings{Baumann, author = {Baumann, Timo}, title = {Large-Scale Speaker Ranking from Crowdsourced Pairwise Listener Ratings}, series = {Interspeech 2017 : 20-24 August 2017}, booktitle = {Interspeech 2017 : 20-24 August 2017}, publisher = {ISCA}, doi = {10.21437/Interspeech.2017-1697}, pages = {2262 -- 2266}, abstract = {Speech quality and likability is a multi-faceted phenomenon consisting of a combination of perceptory features that cannot easily be computed nor weighed automatically. Yet, it is often easy to decide which of two voices one likes better, even though it would be hard to describe why, or to name the underlying basic perceptory features. Although likability is inherently subjective and individual preferences differ frequently, generalizations are useful and there is often a broad intersubjective consensus about whether one speaker is more likable than another. However, breaking down likability rankings into pairwise comparisons leads to a quadratic explosion of rating pairs. We present a methodology and software to efficiently create a likability ranking for many speakers from crowdsourced pairwise likability ratings. We collected pairwise likability ratings for many (>220) speakers from many raters (>160) and turn these ratings into one likability ranking. We investigate the resulting speaker ranking stability under different conditions: limiting the number of ratings and the dependence on rater and speaker characteristics. We also analyze the ranking wrt. acoustic correlates to find out what factors influence likability. We publish our ranking and the underlying ratings in order to facilitate further research.}, language = {en} }