  Modern speaker verification (SV) systems typically demand expensive storage
and computing resources, thereby hindering their deployment on mobile devices.
In this paper, we explore adaptive neural network quantization for lightweight
speaker verification. Firstly, we propose a novel adaptive uniform precision
quantization method which enables the dynamic generation of quantization
centroids customized for each network layer based on k-means clustering. By
applying it to the pre-trained SV systems, we obtain a series of quantized
variants with different bit widths. To enhance the performance of low-bit
quantized models, a mixed precision quantization algorithm along with a
multi-stage fine-tuning (MSFT) strategy is further introduced. Unlike uniform
precision quantization, mixed precision approach allows for the assignment of
varying bit widths to different network layers. When bit combination is
determined, MSFT is employed to progressively quantize and fine-tune network in
a specific order. Finally, we design two distinct binary quantization schemes
to mitigate performance degradation of 1-bit quantized models: the static and
adaptive quantizers. Experiments on VoxCeleb demonstrate that lossless 4-bit
uniform precision quantization is achieved on both ResNets and DF-ResNets,
yielding a promising compression ratio of around 8. Moreover, compared to
uniform precision approach, mixed precision quantization not only obtains
additional performance improvements with a similar model size but also offers
the flexibility to generate bit combination for any desirable model size. In
addition, our suggested 1-bit quantization schemes remarkably boost the
performance of binarized models. Finally, a thorough comparison with existing
lightweight SV systems reveals that our proposed models outperform all previous
methods by a large margin across various model size ranges.
