Using Crowdsourcing for Labelling Emotional Speech Assets

The success of supervised learning approaches for the classification of emotion in speech depends highly on the quality of the training data. The manual annotation of emotion speech assets is the primary way of gathering training data for emotional speech recognition. This position paper proposes the use of crowdsourcing for the rating of emotion speech assets. Recent developments in learning from crowdsourcing offer opportunities to determine accurate ratings for assets which have been annotated by large numbers of non-expert individuals. The challenges involved include identifying good annotators, determining consensus ratings and learning the bias of annotators

Estimating the scale of stone axe production: A case study from Onega Lake, Russian Karelia

The industry of metatuff axes and adzes on the western coast of Onega Lake (Eneolithic period, ca. 3500 – 1500 cal. BC) allows assuming some sort of craft specialization. Excavations of a workshop site Fofanovo XIII, conducted in 2010-2011, provided an extremely large assemblage of artefacts (over 350000 finds from just 30 m2, mostly production debitage). An attempt to estimate the output of production within the excavated area is based on experimental data from a series of replication experiments. Mass-analysis with the aid of image recognition software was used to obtain raw data from flakes from excavations and experiments. Statistical evaluation assures that the experimental results can be used as a basement for calculations. According to the proposed estimation, some 500 – 1000 tools could have been produced here, and this can be qualified as an evidence of “mass-production”

Context Cues For Classification Of Competitive And Collaborative Overlaps

Being able to respond appropriately to users’ overlaps should be seen as one of the core competencies of incremental dialogue systems. At the same time identifying whether an interlocutor wants to support or grab the turn is a task which comes naturally to humans, but has not yet been implemented in such systems. Motivated by this we first investigate whether prosodic characteristics of speech in the vicinity of overlaps are significantly different from prosodic characteristics in the vicinity of non-overlapping speech. We then test the suitability of different context sizes, both preceding and following but excluding features of the overlap, for the automatic classification of collaborative and competitive overlaps. We also test whether the fusion of preceding and succeeding contexts improves the classification. Preliminary results indicate that the optimal context for classification of overlap lies at 0.2 seconds preceding the overlap and up to 0.3 seconds following it. We demonstrate that we are able to classify collaborative and competitive overlap with a median accuracy of 63%

Collinear matching for Sivers function at next-to-leading order.

We evaluate the light-cone operator product expansion for unpolarized transverse momentum dependent (TMD) operator in the background-field technique up twist-3 inclusively. The next-to-leading order (NLO) matching coefficient for the Sivers function is derived. The method, as well as many details of the calculation are presented