16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Speech-Based Location Estimation of First Responders in a Simulated Search and Rescue Scenario

Saeid Mokaram, Roger K. Moore

University of Sheffield, UK

In our research, we explore possible solutions for extracting valuable information about first responders' (FR) location from speech communication channels during crisis response. Fine-grained identification of fundamental units of meaning (e. g. sentences, named entities and dialogue acts) is sensitive to high error rate in automatic transcriptions of noisy speech. However, looking from a topic-based perspective and utilizing text vectorization techniques such as Latent Dirichlet Allocation (LDA) make this more robust to such errors. In this paper, the location estimation problem is framed as a topic segmentation task on FRs' spoken reports about their observations and actions. Identifying the changes in the content of a report over time is an indication that the speaker has moved from one particular location to another. This provides an estimation about the location of the speaker. A goal-oriented human/human conversational speech corpus was collected based on an abstract communication model between FR and task leader during a search process in a simulation environment. Results show the effectiveness of a topic-based approach and especially low sensitivity of the LDA-based method to the highly imperfect automatic transcriptions.

Full Paper

Bibliographic reference.  Mokaram, Saeid / Moore, Roger K. (2015): "Speech-based location estimation of first responders in a simulated search and rescue scenario", In INTERSPEECH-2015, 2734-2738.