MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms
<|reference_start|>MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms: In speaker verification systems, the utilization of short utterances presents a persistent challenge, leading to performance degradation primarily due to insufficient phonetic information to characterize the speakers. To overcome this obstacle, we propose a novel structure, MR-RawNet, designed to enhance the robustness of speaker verification systems against variable duration utterances using raw waveforms. The MR-RawNet extracts time-frequency representations from raw waveforms via a multi-resolution feature extractor that optimally adjusts both temporal and spectral resolutions simultaneously. Furthermore, we apply a multi-resolution attention block that focuses on diverse and extensive temporal contexts, ensuring robustness against changes in utterance length. The experimental results, conducted on VoxCeleb1 dataset, demonstrate that the MR-RawNet exhibits superior performance in handling utterances of variable duration compared to other raw waveform-based systems.<|reference_end|>
@article{kim2024mr-rawnet:,
    title={MR-RawNet: Speaker verification system with multiple temporal
  resolutions for variable duration utterances using raw waveforms},
    author={Seung-bin Kim, Chan-yeong Lim, Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin,
  Kyo-Won Koo, Ha-Jin Yu},
    journal={arXiv preprint arXiv:2406.07103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07103},
    primaryClass={eess.AS cs.AI}
}