GatherMOS uses LLMs as meta-evaluators to aggregate acoustic features and pseudo-labels for improved mean opinion score prediction in few-shot speech quality assessment.
The proof and measurement of associ- ation between two things,
2 Pith papers cite this work. Polarity classification is still indexing.
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Speaker-aware modeling of conversational timing using per-speaker deviation distributions, Markov turn-taking, and unified KDE gap modeling improves alignment with real Switchboard patterns over independence-based baselines.
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Few-Shot and Pseudo-Label Guided Speech Quality Evaluation with Large Language Models
GatherMOS uses LLMs as meta-evaluators to aggregate acoustic features and pseudo-labels for improved mean opinion score prediction in few-shot speech quality assessment.
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From Independence to Interaction: Speaker-Aware Simulation of Multi-Speaker Conversational Timing
Speaker-aware modeling of conversational timing using per-speaker deviation distributions, Markov turn-taking, and unified KDE gap modeling improves alignment with real Switchboard patterns over independence-based baselines.