MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms[link]
Seung-bin Kim, Chan-yeong Lim, Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin, Kyo-Won Koo, and Ha-Jin Yu
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 waveformbased systems.
MR-RawNet: Speaker verification system with multiple temporal resolutions for variable duration utterances using raw waveforms[link]
Seung-bin Kim, Chan-yeong Lim, Jungwoo Heo, Ju-ho Kim, Hyun-seo Shin, Kyo-Won Koo, and Ha-Jin Yu
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 waveformbased systems.