RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification

By Jee-weon Jung, Hee-Soo Heo, Ju-ho Kim, Hye-jin Shim, Ha-jin Yu. 2019

164 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Icassp 2022 Deep Noise Suppression Challenge (2022, methodology): “We extract speaker embed-dings for each enrollment audio using a baseline RawNet2 speaker model [10] which is adopted as the baseline speaker embedding extractor for this challenge.”
End-to-End anti-spoofing with RawNet2 (2020, methodology): “Among other early solutions is RawNet [9], introduced in 2019.”
Improved RawNet with Filter-wise Rescaling for Text-independent Speaker Verification using Raw Waveforms (2020, methodology): “For example, RawNet [1] extracts speaker embeddings from raw waveforms, which simplifies the process pipeline and demonstrates competitive performance.”