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arXiv · 2206.12857

Transport-Oriented Feature Aggregation for Speaker Embedding Learning

Yusheng Tian·Jingyu Li·Tan Lee
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Abstract

Pooling is needed to aggregate frame-level features into utterance-level representations for speaker modeling. Given the success of statistics-based pooling methods, we hypothesize that speaker characteristics are well represented in the statistical distribution over the pre-aggregation layer's output, and propose to use transport-oriented feature aggregation for deriving speaker embeddings. The aggregated representation encodes the geometric structure of the underlying feature distribution, which is expected to contain valuable speaker-specific information that may not be represented by the commonly used statistical measures like mean and variance. The original transport-oriented feature aggregation is also extended to a weighted-frame version to incorporate the attention mechanism. Experiments on speaker verification with the Voxceleb dataset show improvement over statistics pooling and its attentive variant.

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Yusheng Tian, Jingyu Li, Tan Lee. 2022-06-26. Transport-Oriented Feature Aggregation for Speaker Embedding Learning. https://arxiv.org/abs/2206.12857

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