Speaker Representation Learning using Global Context Guided Channel and Time-Frequency Transformations

2 Sep 2020 Wei Xia John H. L. Hansen

In this study, we propose the global context guided channel and time-frequency transformations to model the long-range, non-local time-frequency dependencies and channel variances in speaker representations. We use the global context information to enhance important channels and recalibrate salient time-frequency locations by computing the similarity between the global context and local features... (read more)

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