Semantic Analysis of Speaker Personality Traits for Voice User Interfaces of Intelligent Systems

Abstract

Modern speech interfaces have achieved remarkable advancements in intelligibility, naturalness of synthesized speech, and accuracy of speech recognition, enabling seamless integration into everyday life. These capabilities underpin the growing prevalence of voice-based interactions with artificial intelligence (AI) systems. However, enhancing user-machine interaction requires addressing empathy and personalization, adapting AI responses to users socio-cultural, professional, and psychological traits. This is particularly relevant as anthropomorphic robotic systems emerge, where voice may serve as the sole communication channel. This paper explores how semantic analysis of vocal characteristics can infer personal traits, leveraging open semantic technologies to improve intelligent systems adaptability and empathy. Speech interfaces (SI) or voice user interfaces (VUI) have evolved significantly, with text-to-speech (TTS) systems achieving high naturalness and speech-to-text (STT) systems boasting impressive recognition accuracy [1]. These advancements, driven by deep learning and large-scale datasets, have made voice a primary modality for interacting with AI, from virtual assistants like Siri and Alexa to cutting-edge models like ChatGPT with voice capabilities [2]. Currently, such systems are ubiquitous, facilitating tasks from ordinary scheduling to complex problem solving [3].