2017 INTERSPEECH INTERSPEECH 2017

Team ELISA System for DARPA LORELEI Speech Evaluation 2016

Abstract

In this paper, we describe the system designed and developed by team ELISA for DARPA’s LORELEI (Low Resource Languages for Emergent Incidents) pilot speech evaluation. The goal of the LORELEI program is to guide rapid resource deployment for humanitarian relief (e.g. for natural disasters), with a focus on “low-resource” language locations, where the cost of developing technologies for automated human language tools can be prohibitive both in monetary terms and timewise. In this phase of the program, the speech evaluation consisted of three separate tasks: detecting presence of an incident, classifying incident type, and classifying incident type along with identifying the location where it occurs. The performance metric was area under curve of precision-recall curves. Team ELISA competed against five other teams and won all the subtasks.

👥 Mega-Team — 20 authors
🧭 Keyword Pioneer — incident detection
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio