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New research evaluates speech quality metrics for neural audio codecs

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates 45 objective speech quality metrics to determine their reliability for assessing neural audio codecs. The study found that neural-based metrics, specifically scoreq and utmos, demonstrated the highest correlation with subjective listening scores under clean speech conditions. However, the research also indicated that non-intrusive metrics tend to become less effective when evaluating very high levels of subjective speech quality. AI

IMPACT This research could lead to more accurate and reliable evaluation of neural audio codecs, potentially improving the quality of AI-generated speech and audio processing.

RANK_REASON The cluster contains an academic paper detailing research findings on speech quality metrics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research evaluates speech quality metrics for neural audio codecs

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wolfgang Mack, Nezih Topaloglu, Laura Lechler, Ivana Bali\'c, Alexandra Craciun, Mansur Yesilbursa, Kamil Wojcicki ·

    Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions

    arXiv:2509.24457v1 Announce Type: cross Abstract: Objective speech-quality metrics are widely used to assess codec performance. However, for neural codecs, it is often unclear which metrics provide reliable quality estimates. To address this, we evaluated 45 objective metrics by …

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