Algorithms for Developing Voice Assistants in Corporate Digital Libraries and Telecommunication Information Systems
Abstract
The paper is a detailed research on voice assistant algorithm development in the case of corporate digital libraries and telecommunication information systems. The suggested modular structure unites the speech-to-text, natural language understanding, and text-to-speech synthesis, which is supplemented by mathematical statements like Mel-Frequency Cepstral Coefficients (MFCC), Hidden Markov Models (HMM), and reinforcement learning to deal with adaptive queries. In contrast to the traditional solutions based on relying on available libraries only, the present research proposes a formal algorithmic design, specific to the field of optical network-based information retrieval of corporations and telecommunication infrastructures. The experimental test portrays a higher capabilities of efficiency in catalog search, strength in noisy conditions, and inclusivity of visually impaired users. Moreover, the system adopts principles of optical communication and photonics to promote the growth of data transmission seamlessly, scalability and greater interoperability among the applications of intelligent machines. This input reinforces the connection between telecommunication technologies, computerized information systems and voice interaction powered by AI.