Exploiting active-learning strategies for annotating prosodic events with limited labeled data

Many applications of spoken-language systems can benefit from having access to annotations of prosodic events. Unfortunately, obtaining human annotations of these events, even sensible amounts to train a supervised system, can become a laborious and costly effort. Given these constraints, this task serves as a good case study for approaches that judiciously guide the selection of data in order to maximize the gain from the human-labeling process or which minimize the size of the training set. To address this, we explore active learning techniques with the objective of reducing the amount of human-annotated data needed to attain a given level of performance. We review strategies that can be used to guide the selection of sequences by combining the output of a classifier and information about the structure of the data into a criterion that can be used during the learning process to query the label of data points that are both informative and representative of the task, and show that f...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Raul Fernandez, Bhuvana Ramabhadran
• Keywords: Active Selection Strategies, ICASSP 2011, Random Data Selection, Selection, Signal Processing