Locally-connected and convolutional neural networks for small footprint speaker recognition

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Pay Less Attention with Lightweight and Dynamic Convolutions (2019, background): “Dynamic convolutions are similar to locally connected layers in the sense that the weights change at every position, however, the difference is that weights are dynamically generated by the model rather than ﬁxed after training (LeCun et al., 1998; Taigman et al., 2014; Chen et al., 2015).”
Generalized End-to-End Loss for Speaker Verification (2017, background): “In recent years, more efforts have been focusing on using neural networks for speaker verification, while the most successful systems use end-to-end training [8, 9, 10, 11, 12].”
End-to-end text-dependent speaker verification (2015, background): “Note that compared to [15], we have improved our d-vectors significantly [4].”
Personalized speech recognition on mobile devices (2016, methodology): “SVD has elsewhere been shown to be effective for speech processing tasks [4, 6, 7] as have structured transforms [8] and low-rank matrix factorizations [9].”
Speaker Diarization with LSTM (2017, background): “In recent years, neural network based audio embeddings (d-vectors) have seen wide-spread use in speaker veriﬁcation applications [7, 8, 9, 10, 11], often signiﬁcantly outperforming previously state-of-the-art techniques based on i-vectors.”