Light-Weight Speaker Verification with Global Context Information
In this paper, we propose a light-weight speaker verification (SV) system that utilizes the characteristics of utterance-level global features.
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.
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|324||International Conference||Miseul Kim, Zhenyu Piao, Seyun Um, Ran Lee, Jaemin Joh, Seungshin Lee, Hong-Goo Kang "Light-Weight Speaker Verification with Global Context Information" in INTERSPEECH, 2022|
|323||International Journal||Kyungguen Byun, Se-yun Um, Hong-Goo Kang "Length-Normalized Representation Learning for Speech Signals" in IEEE Access, vol.10, pp.60362-60372, 2022|
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|319||International Conference||Jinyoung Lee and Hong-Goo Kang "Stacked U-Net with High-level Feature Transfer for Parameter Efficient Speech Enhancement" in APSIPA ASC, 2021|
|318||International Conference||Huu-Kim Nguyen, Kihyuk Jeong, Se-Yun Um, Min-Jae Hwang, Eunwoo Song, Hong-Goo Kang "LiteTTS: A Decoder-free Light-weight Text-to-wave Synthesis Based on Generative Adversarial Networks" in INTERSPEECH, 2021|
|317||International Conference||Zainab Alhakeem, Yoohwan Kwon, Hong-Goo Kang "Disentangled Representations for Arabic Dialect Identification based on Supervised Clustering with Triplet Loss" in EUSIPCO, 2021|