Papers
Light-Weight Speaker Verification with Global Context Information
International Conference
작성자
dsp
작성일
2022-06-16 17:03
조회
2143
Many recent SV tasks employ convolutional neural networks (CNNs) to extract representative speaker features from the given input utterances. However, their inherent receptive field size on the feature extraction process is limited by the localized structure of the convolutional layers.
To effectively extract utterance-level global speaker representations, we introduce a novel architecture combining a CNN with a self-attention network that is able to utilize the relationship between local and aggregated global features. The global features are continuously updated at every analysis block using a point-wise attentive summation to the local features.
We also adopt a densely connected CNN structure (DenseNet) to reliably estimate speaker-related local features with a small number of model parameters. Our proposed model shows higher speaker verification performance with EER 1.935% with significantly small number of parameters, 1.2M, which is 16% reduced model size than the baseline models.
전체 367
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323 | International Journal | Kyungguen Byun, Seyun Um, Hong-Goo Kang "Length-Normalized Representation Learning for Speech Signals" in IEEE Access, vol.10, pp.60362-60372, 2022 | |
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