TY - JOUR A1 - Baumann, Timo A1 - Köhn, Arne A1 - Hennig, Felix T1 - The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening JF - Language Resources and Evaluation N2 - Spoken corpora are important for speech research, but are expensive to create and do not necessarily reflect (read or spontaneous) speech ‘in the wild’. We report on our conversion of the preexisting and freely available Spoken Wikipedia into a speech resource. The Spoken Wikipedia project unites volunteer readers of Wikipedia articles. There are initiatives to create and sustain Spoken Wikipedia versions in many languages and hence the available data grows over time. Thousands of spoken articles are available to users who prefer a spoken over the written version. We turn these semi-structured collections into structured and time-aligned corpora, keeping the exact correspondence with the original hypertext as well as all available metadata. Thus, we make the Spoken Wikipedia accessible for sustainable research. We present our open-source software pipeline that downloads, extracts, normalizes and text–speech aligns the Spoken Wikipedia. Additional language versions can be exploited by adapting configuration files or extending the software if necessary for language peculiarities. We also present and analyze the resulting corpora for German, English, and Dutch, which presently total 1005 h and grow at an estimated 87 h per year. The corpora, together with our software, are available via http://islrn.org/resources/684-927-624-257-3/. As a prototype usage of the time-aligned corpus, we describe an experiment about the preferred modalities for interacting with information-rich read-out hypertext. We find alignments to help improve user experience and factual information access by enabling targeted interaction. KW - Annotation KW - Eyes-free speech access KW - Found data KW - Robust text–speech alignment KW - Speech corpus KW - Spoken hypertext KW - Wikipedia Y1 - 2019 U6 - https://doi.org/10.1007/s10579-017-9410-y VL - 53 IS - 2 SP - 303 EP - 329 PB - Springer Nature ER - TY - CHAP A1 - Baumann, Timo ED - Weiss, Benjamin ED - Trouvain, Jürgen ED - Barkat-Defradas, Mélissa ED - Ohala, John J. T1 - Ranking and Comparing Speakers Based on Crowdsourced Pairwise Listener Ratings T2 - Voice attractiveness: Studies on Sexy, Likable, and Charismatic Speakers N2 - 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, generalizations are useful and there is often a broad intersubjective consensus about whether one speaker is more likeable than another. We present a methodology to efficiently create a likability ranking for many speakers from crowdsourced pairwise likability ratings which focuses manual rating effort on pairs of similar quality using an active sampling technique. Using this methodology, we collected pairwise likability ratings for many speakers (>220) from many raters (>160). We analyze listener preferences by correlating the resulting ranking with various acoustic and prosodic features. We also present a neural network that is able to model the complexity of listener preferences and the underlying temporal evolution of features. The recurrent neural network achieves remarkably high performance in estimating the pairwise decisions and an ablation study points toward the criticality of modeling temporal aspects in speech quality assessment. KW - Crowdsourcing KW - Found data KW - Likability ratings KW - Ranking KW - Sequence modelling KW - Speech quality Y1 - 2021 SN - 978-981-15-6626-4 SN - 978-981-15-6627-1 U6 - https://doi.org/10.1007/978-981-15-6627-1_14 SP - 263 EP - 279 PB - Springer CY - Singapore ER -