How Many Labellers Revisited – Naı̈ves, Experts, and Real Experts
نویسندگان
چکیده
A database of non-native German productions was annotated by three different groups: by experts using detailed, localised labels as well as coarse, global labels, and by phoneticians and naı̈ve subjects, using the same coarse global labels. For the detailed annotation, segmental and supra-segmental labels were given segment-based and word-based. The global annotation consisted of a turn-based assessment of intelligibility, nonnative accent, melody, and rhythm. Moreover, we use a large, specialised prosodic feature vector for modelling native vs. nonnative speech. We study relationships between detailed and global labels, analyse the quality of expert and naı̈ve labellers, and present an automatic system for predicting a speaker’s score for the global labels.
منابع مشابه
How many labellers revisited - naïves, experts, and real experts
A database of non-native German productions was annotated by three different groups: by experts using detailed, localised labels as well as coarse, global labels, and by phoneticians and naı̈ve subjects, using the same coarse global labels. For the detailed annotation, segmental and supra-segmental labels were given segment-based and word-based. The global annotation consisted of a turn-based as...
متن کاملConvergence Rates for Mixture-of-Experts
In mixtures-of-experts (ME) model, where a number of submodels (experts) are combined, there have been two longstanding problems: (i) how many experts should be chosen, given the size of the training data? (ii) given the total number of parameters, is it better to use a few very complex experts, or is it better to combine many simple experts? In this paper, we try to provide some insights to th...
متن کاملCrowdsourcing via Tensor Augmentation and Completion
Nowadays, the rapid proliferation of data makes it possible to build complex models for many real applications. Such models, however, usually require large amount of labeled data, and the labeling process can be both expensive and tedious for domain experts. To address this problem, researchers have resorted to crowdsourcing to collect labels from non-experts with much less cost. The key challe...
متن کاملInferring Ground Truth from Subjective Labelling of Venus Images
In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper...
متن کاملMusic Genre Classification Revisited: An In-Depth Examination Guided by Music Experts
Despite their many identified shortcomings, music genres are still often used as ground truth and as a proxy for music similarity. In this work we therefore take another in-depth look at genre classification, this time with the help of music experts. In comparison to existing work, we aim at including the viewpoint of different stakeholders to investigate whether musicians and end-user music ta...
متن کامل