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arXiv · 2406.07103

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

Seung-bin Kim·Chan-yeong Lim·Jungwoo Heo·Ju-ho Kim·Hyun-seo Shin·Kyo-Won Koo·Ha-Jin Yu
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Abstract

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.

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Seung-bin Kim, Chan-yeong Lim, Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin, Kyo-Won Koo, Ha-Jin Yu. 2024-06-11. MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms. https://arxiv.org/abs/2406.07103

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