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Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions — arXiv:2509.24457

Source: arXiv (en)
Original: https://arxiv.org/abs/2509.24457
License (as archived): CC0-1.0
Archived: 2026-10-31T12:17:21.105Z
SHA-256 of the archived text: b283bffd510336604a0e391c8e2e3528b2276bf8e77a34e0e47daf5096d6efb5
Stable URL of this page: https://metraipolis.com/documento/dc_bf73fa82209c

How to cite

Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions — arXiv:2509.24457 — arXiv (en), https://arxiv.org/abs/2509.24457 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf73fa82209c (Metraipolis archive dc_bf73fa82209c, sha256 b283bffd510336604a0e391c8e2e3528b2276bf8e77a34e0e47daf5096d6efb5)

BibTeX

@misc{dcbf73fa82209c,
  title = {Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions — arXiv:2509.24457},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2509.24457},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf73fa82209c; sha256 b283bffd510336604a0e391c8e2e3528b2276bf8e77a34e0e47daf5096d6efb5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2509.24457. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 correlating their scores with subjective listening scores for clean speech across 17 codec conditions. Neural-based metrics such as scoreq and utmos achieved the highest Pearson correlations with subjective scores. Further analysis across different subjective quality ranges revealed that non-intrusive metrics tend to saturate at high subjective quality levels.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-05 in cs.AI).
Categorie arXiv: Audio and Speech Processing (eess.AS) ; Artificial Intelligence (cs.AI); Sound (cs.SD); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2509.24457 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-31T12:05:52.879Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-05?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).