Evaluating Objective Speech Quality Metrics for Neural Audio Codecs
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918217444753408 |
|---|---|
| author | Lanzendörfer, Luca A. Grötschla, Florian |
| author_facet | Lanzendörfer, Luca A. Grötschla, Florian |
| contents | Neural audio codecs have gained recent popularity for their use in generative modeling as they offer high-fidelity audio reconstruction at low bitrates. While human listening studies remain the gold standard for assessing perceptual quality, they are time-consuming and impractical. In this work, we examine the reliability of existing objective quality metrics in assessing the performance of recent neural audio codecs. To this end, we conduct a MUSHRA listening test on high-fidelity speech signals and analyze the correlation between subjective scores and widely used objective metrics. Our results show that, while some metrics align well with human perception, others struggle to capture relevant distortions. Our findings provide practical guidance for selecting appropriate evaluation metrics when using neural audio codecs for speech. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Objective Speech Quality Metrics for Neural Audio Codecs Lanzendörfer, Luca A. Grötschla, Florian Sound Neural audio codecs have gained recent popularity for their use in generative modeling as they offer high-fidelity audio reconstruction at low bitrates. While human listening studies remain the gold standard for assessing perceptual quality, they are time-consuming and impractical. In this work, we examine the reliability of existing objective quality metrics in assessing the performance of recent neural audio codecs. To this end, we conduct a MUSHRA listening test on high-fidelity speech signals and analyze the correlation between subjective scores and widely used objective metrics. Our results show that, while some metrics align well with human perception, others struggle to capture relevant distortions. Our findings provide practical guidance for selecting appropriate evaluation metrics when using neural audio codecs for speech. |
| title | Evaluating Objective Speech Quality Metrics for Neural Audio Codecs |
| topic | Sound |
| url | https://arxiv.org/abs/2511.19734 |