Optimizing Voice Recognition Informatic Robots for Effective Communication in Outpatient Settings.

Abstract

Aim/Objective Within the dynamic healthcare technology landscape, this research aims to explore patient inquiries within outpatient clinics, elucidating the interplay between technology and healthcare intricacies. Building upon the initial intelligent guidance robot implementation shortcomings, this investigation seeks to enhance informatic robots with voice recognition technology. The objective is to analyze users' vocal patterns, discern age-associated vocal attributes, and facilitate age differentiation through subtle vocal nuances to enhance the efficacy of human-robot communication within outpatient clinical settings. Methods This investigation employs a multi-faceted approach. It leverages voice recognition technology to analyze users' vocal patterns. A diverse dataset of voice samples from various age groups was collected. Acoustic features encompassing pitch, formant frequencies, spectral characteristics, and vocal tract length are extracted from the audio samples. The Mel Filterbank and Mel-Frequency Cepstral Coefficients (MFCCs) are employed for speech and audio processing tasks alongside machine learning algorithms to assess and match vocal patterns to age-related traits. Results The research reveals compelling outcomes. The incorporation of voice recognition technology contributes to a significant improvement in human-robot communication within outpatient clinical settings. Through accurate analysis of vocal patterns and age-related traits, informatic robots can differentiate age through nuanced verbal cues. This augmentation leads to enhanced contextual understanding and tailored responses, significantly advancing the efficiency of patient interactions with the robots. Conclusion Integrating voice recognition technology into informatic robots presents a noteworthy advancement in outpatient clinic settings. By enabling age differentiation through vocal nuances, this augmentation enhances the precision and relevance of responses. The study contributes to the ongoing discourse on the dynamic evolution of healthcare technology, underscoring the complex synergy between technological progression and the intricate realities within healthcare infrastructure. As healthcare continues to metamorphose, the seamless integration of voice recognition technology marks a pivotal stride in optimizing human-robot communication and elevating patient care within outpatient settings.