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Speech-based location estimation of first responders in a simulated search and rescue scenario

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.