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.

通过使用多分辨率特征提取器和多分辨率注意力块，MR-RawNet 能够从原始波形中提取时间 - 频率表示，增强说话人验证系统对可变持续时间的音频语句的抗干扰能力。实验结果表明，与其他基于原始波形的系统相比，MR-RawNet 在处理可变持续时间的音频语句时表现出更高的性能。

MR-RawNet：使用原始波形的多时间分辨率变长语音说话人验证系统

MR-RawNet: Speaker verification system with multiple temporal  resolutions for variable duration utterances using raw waveforms

The SdSv challenge Task 2 provided an opportunity to assess efficiency and
robustness of modern text-independent speaker verification systems. But it also
made it possible to test new approaches, capable of taking into account the
main issues of this challenge (duration, language, ...). This paper describes
the contributions of our laboratory to the speaker recognition field. These
contributions highlight two other challenges in addition to short-duration and
language: the mismatch between enrollment and test data and the one between
subsets of the evaluation trial dataset. The proposed approaches experimentally
show their relevance and efficiency on the SdSv evaluation, and could be of
interest in many real-life applications.

本研究描述了我们实验室在发言者识别领域的贡献，突出了除了短时长和语言之外，注册和测试数据之间以及评估数据集子集之间的不匹配问题。提出的方法在 SdSv 评估中实验性地展示了它们的相关性和效率，并可能在许多实际应用中具有重要意义。