The rapid diffusion of voice-enabled and conversational systems has intensified the need for tightly integrated architectures that combine Automatic Speech Recognition (ASR), semantic representation mechanisms, and Large Language Models (LLMs). Although each component has advanced substantially, their structural interdependencies within Automated Voice Query Response Systems (AVQRS) remain underexplored. This study investigates the relationships among ASR quality, semantic embedding fidelity, query clustering coherence, LLM integration quality, and overall system performance. It further evaluates whether LLM model scale (parameter size) and context-window capacity moderate response quality and latency. A random subsample of 300 cases was drawn from a synthetic benchmark dataset (N = 1,000) comprising 20 measured indicators representing latent AVQRS constructs. Statistical procedures included descriptive analysis, Pearson correlation, multiple regression (R²), independent-samples t-tests, one-way ANOVA, and Shapiro–Wilk normality testing across six predefined hypotheses. Results indicated normally distributed performance scores (W = 0.988, p = .882). Construct means were high (e.g., ASR accuracy M = 0.899; semantic embedding quality M = 0.846; overall performance M = 0.864), suggesting strong component-level functionality. However, inter-construct correlations were weak, and regression explained less than 1% of variance in system performance (R² = .005). Neither LLM model size (≤13B, 14–35B, >35B) nor context-window configuration (2,048–8,192 tokens) significantly influenced performance or latency. The findings suggest that isolated component metrics may not capture integrative system dynamics. The study underscores the necessity of theoretically grounded operationalisation and end-to-end, task-based evaluation frameworks, recommending confirmatory structural equation modelling using real-world deployment data.