# ESCAPE - Echo SCraper and ClAssifier of PErsons: A novel tool to facilitate using voice-controlled devices for research

## TL;DR

ESCAPE is an open-source tool that extracts and classifies speaker data from Amazon Echo recordings, enabling research on individual behavior and health in naturalistic home settings with minimal intrusion.

## Contribution

The paper introduces ESCAPE, a novel software for extracting and classifying speaker data from Amazon Echo interactions, facilitating research in naturalistic environments.

## Key findings

- ESCAPE successfully extracts interaction data from Amazon Echo devices.
- The classifier accurately identifies speakers with limited labeled data.
- Application of ESCAPE over 3 months demonstrates its utility in real-world settings.

## Abstract

Smart devices have become common place in many homes, and these devices can be utilized to provide support for people with mental or physical deficits. Voice-controlled assistants are a class of smart device that collect a large amount of data in the home. In this work we present Echo SCraper and ClAssifier of Persons (ESCAPE), an open source software for the extraction of Amazon Echo interaction data, and speaker recognition on that data. We show that ESCAPE is able to extract data from a voice-controlled assistant and classify with accuracy who is talking, based on a small number of labeled audio data. Using ESCAPE to extract interactions recorded over 3 months in the first author's home yields a rich dataset of transcribed audio recordings. Our results demonstrate that using this software the Amazon Echo can be used to study participants in a naturalistic setting with minimal intrusion. We also discuss the potential for usage of voice-controlled devices together with ESCAPE to understand how diseases affect individuals, and how these data can be used to monitor disease processes in general.