  Recently, direct modeling of raw waveforms using deep neural networks has
been widely studied for a number of tasks in audio domains. In speaker
verification, however, utilization of raw waveforms is in its preliminary
phase, requiring further investigation. In this study, we explore end-to-end
deep neural networks that input raw waveforms to improve various aspects:
front-end speaker embedding extraction including model architecture,
pre-training scheme, additional objective functions, and back-end
classification. Adjustment of model architecture using a pre-training scheme
can extract speaker embeddings, giving a significant improvement in
performance. Additional objective functions simplify the process of extracting
speaker embeddings by merging conventional two-phase processes: extracting
utterance-level features such as i-vectors or x-vectors and the feature
enhancement phase, e.g., linear discriminant analysis. Effective back-end
classification models that suit the proposed speaker embedding are also
explored. We propose an end-to-end system that comprises two deep neural
networks, one front-end for utterance-level speaker embedding extraction and
the other for back-end classification. Experiments conducted on the VoxCeleb1
dataset demonstrate that the proposed model achieves state-of-the-art
performance among systems without data augmentation. The proposed system is
also comparable to the state-of-the-art x-vector system that adopts data
augmentation.
