Машинне навчання у спеціалізованих голосових системах

Abstract

Machine learning has become a cornerstone in the development of modern voice assistants, enabling them to move beyond simple command execution toward complex, context-aware interactions. While mainstream systems such as Alexa, Siri, and Google Assistant rely on large-scale datasets and general-purpose models, specialized voice systems require tailored approaches. Supervised learning allows classification of user queries, reinforcement learning optimizes responses through feedback, and unsupervised learning uncovers hidden patterns in organizational workflows. These methods, combined with explainable AI and low-data learning techniques, make specialized assistants adaptable to diverse communities