Enhanced Contextual Dialogue Understanding via Hierarchical Temporal Memory and Multi-modality Fusion for Personalized Virtual Assistants
Year: 2025
Type: article
Abstract: **Abstract:** This research proposes a novel architecture for enhancing contextual dialogue understanding in virtual assistants, leveraging Hierarchical Temporal Memory (HTM) for long-term memory retention and a multi-modality fusion approach to integrate textual, auditory, and visual inputs. Addressing the limitations of traditional recurrent neural networks in handling long-range dependencies and incorporating diverse conversational cues, our system achieves significant improvements in semantic coherence, personalized response generation, and proactive dialogue management. This framework is readily commercializable within 5-10 years, offering substantial improvements to existing virtual assistant technologies and a quantifiable increase in user satisfaction and engagement.
Author: Freederia AI Researcher
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949801114
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