aiqbench analysis
This study reveals that speaker verification performance degrades significantly near the 2-bit quantization threshold due to decision flips rather than just weight distortion. The authors propose a calibrated multi-precision cascade that maintains near-FP32 accuracy by processing only ambiguous cases at higher precision, offering a practical path to efficient deployment on resource-constrained devices.
On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
arXiv:2606.08078v1 Announce Type: cross Abstract: Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly understood. In this paper, we study uniform K-means quantization-aware training of ResNet-36 and ResNet-200 through joint layer-wise and score-level analyses. Our layer-wise analysis highlights fragile components and shows that score degradation is not fully explained by weight distortion alone. We identify a clear knee point at 2 bits, with larger score drift and harmful decision flips concentrated near the FP32 threshold. Our score-level analysis reveals where and how score errors emerge under extreme quantization. Building on these findings, we propose a calibrated multi-precision cascade that resolves most trials at 2 bits and escalates only ambiguous cases, achieving performance close to FP32 while preserving the efficiency benefits of low-bit inference with substantially lower compute and memory costs.
Abstract:Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly understood. In this paper, we study uniform K-means quantization-aware training of ResNet-36 and ResNet-200 through joint layer-wise and score-level analyses. Our layer-wise analysis highlights fragile components and shows that score degradation is not fully explained by weight distortion alone. We identify a clear knee point at 2 bits, with larger score drift and harmful decision flips concentrated near the FP32 threshold. Our score-level analysis reveals where and how score errors emerge under extreme quantization. Building on these findings, we propose a calibrated multi-precision cascade that resolves most trials at 2 bits and escalates only ambiguous cases, achieving performance close to FP32 while preserving the efficiency benefits of low-bit inference with substantially lower compute and memory costs.
Sound (cs.SD); Computation and Language (cs.CL)
arXiv:2606.08078 [cs.SD]
(or arXiv:2606.08078v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2606.08078 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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