Abstract
The continuous deployment of enterprise voice systems creates a critical operational bottleneck: evaluating the acoustic quality of synthetic speech across high-volume production updates. While human Mean Opinion Score (MOS) remains the quality gold standard, it introduces severe operational costs and turn-around delays that make continuous integration infeasible. In this work, we present a system design case study for a scalable, low-cost and offline evaluation pipeline capable of scoring large batches of synthetic speech at marginal cost. First, we use Qwen2-Audio-7B-Instruct in a zero-shot setting for granular MOS estimation, noticing perceptual blind spots and inherent biases. Then, we analyze SOMOS dataset and construct a diagnostic benchmark that operationalizes two prosodic dimensions associated with human naturalness ratings. Finally, we adapt Qwen2-Audio-7B-Instruct through a parameter-efficient finetuning (LoRA) on the task of MOS evaluation, observing improvements over base model and widely used neural baselines like UTMOS and establishing a practical blueprint for enterprise-grade speech quality assurance.
Abstract (translated)
URL
https://arxiv.org/abs/2610.41539
