  Recent advances in deep learning have facilitated the design of speaker
verification systems that directly input raw waveforms. For example, RawNet
extracts speaker embeddings from raw waveforms, which simplifies the process
pipeline and demonstrates competitive performance. In this study, we improve
RawNet by scaling feature maps using various methods. The proposed mechanism
utilizes a scale vector that adopts a sigmoid non-linear function. It refers to
a vector with dimensionality equal to the number of filters in a given feature
map. Using a scale vector, we propose to scale the feature map
multiplicatively, additively, or both. In addition, we investigate replacing
the first convolution layer with the sinc-convolution layer of SincNet.
Experiments performed on the VoxCeleb1 evaluation dataset demonstrate the
effectiveness of the proposed methods, and the best performing system reduces
the equal error rate by half compared to the original RawNet. Expanded
evaluation results obtained using the VoxCeleb1-E and VoxCeleb-H protocols
marginally outperform existing state-of-the-art systems.
