Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.06756 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908398664024064 |
|---|---|
| author | Dutta, Bikash Ranjan, Rishabh Sathvik, Shyam Vatsa, Mayank Singh, Richa |
| author_facet | Dutta, Bikash Ranjan, Rishabh Sathvik, Shyam Vatsa, Mayank Singh, Richa |
| contents | Quantization is essential for deploying large audio language models (LALMs) efficiently in resource-constrained environments. However, its impact on complex tasks, such as zero-shot audio spoofing detection, remains underexplored. This study evaluates the zero-shot capabilities of five LALMs, GAMA, LTU-AS, MERaLiON, Qwen-Audio, and SALMONN, across three distinct datasets: ASVspoof2019, In-the-Wild, and WaveFake, and investigates their robustness to quantization (FP32, FP16, INT8). Despite high initial spoof detection accuracy, our analysis demonstrates severe predictive biases toward spoof classification across all models, rendering their practical performance equivalent to random classification. Interestingly, quantization to FP16 precision resulted in negligible performance degradation compared to FP32, effectively halving memory and computational requirements without materially impacting accuracy. However, INT8 quantization intensified model biases, significantly degrading balanced accuracy. These findings highlight critical architectural limitations and emphasize FP16 quantization as an optimal trade-off, providing guidelines for practical deployment and future model refinement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06756 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can Quantized Audio Language Models Perform Zero-Shot Spoofing Detection? Dutta, Bikash Ranjan, Rishabh Sathvik, Shyam Vatsa, Mayank Singh, Richa Sound Audio and Speech Processing Quantization is essential for deploying large audio language models (LALMs) efficiently in resource-constrained environments. However, its impact on complex tasks, such as zero-shot audio spoofing detection, remains underexplored. This study evaluates the zero-shot capabilities of five LALMs, GAMA, LTU-AS, MERaLiON, Qwen-Audio, and SALMONN, across three distinct datasets: ASVspoof2019, In-the-Wild, and WaveFake, and investigates their robustness to quantization (FP32, FP16, INT8). Despite high initial spoof detection accuracy, our analysis demonstrates severe predictive biases toward spoof classification across all models, rendering their practical performance equivalent to random classification. Interestingly, quantization to FP16 precision resulted in negligible performance degradation compared to FP32, effectively halving memory and computational requirements without materially impacting accuracy. However, INT8 quantization intensified model biases, significantly degrading balanced accuracy. These findings highlight critical architectural limitations and emphasize FP16 quantization as an optimal trade-off, providing guidelines for practical deployment and future model refinement. |
| title | Can Quantized Audio Language Models Perform Zero-Shot Spoofing Detection? |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.06756 |