Voice Recognition Performance Analysis on Smart Speakers Using the Random Forest Method
Abstract
The development of Internet of Things (IoT) and artificial intelligence technology has driven the increasing use of voice user interfaces (VUI) as a more natural form of human-computer interaction. One widely used VUI implementation is voice recognition-based smart speakers. Despite its widespread adoption, voice recognition performance on smart speakers is not necessarily optimal when used in real-world conditions, particularly in far-field scenarios that are influenced by user distance, environmental noise, and system response time. This study aims to analyze and compare the voice recognition performance of Amazon Alexa smart speakers and the Interactive Speaker System as a non-vendor comparison system. Testing was conducted at varying user distances in a non-soundproof room to represent real-world operational conditions.The obtained performance data was analyzed using the Random Forest method as a classification tool due to its ability to handle multivariate data and nonlinear relationships between variables. The results showed that variations in user distance significantly affected the voice recognition performance of both systems, with a tendency for performance to decrease as distance increased. In addition, differences in system architecture characteristics also influenced the level of resilience to environmental conditions. The application of the Random Forest method also enabled the identification of dominant factors that influence the success of voice recognition. This research is expected to provide theoretical contributions in the study of voice recognition performance in far-field scenarios, as well as practical contributions as a basis for consideration in the selection and development of more reliable voice-based interaction systems in real environments.
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