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Short-time spectral aggregation for speaker embedding

Citation Author(s):
Youzhi Tu, Man-Wai Mak
Submitted by:
youzhi tu
Last updated:
23 June 2021 - 6:40am
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Youzhi Tu
Paper Code:
ICASSP-5261

Abstract 

Abstract: 

State-of-the-art speaker verification systems take frame-level acoustics features as input and produce fixed-dimensional embeddings as utterance-level representations. Thus, how to aggregate information from frame-level features is vital for achieving high performance. This paper introduces short-time spectral pooling (STSP) for better aggregation of frame-level information. STSP transforms the temporal feature maps of a speaker embedding network into the spectral domain and extracts the lowest spectral components of the averaged spectrograms for aggregation. Benefiting from the low-pass characteristic of the averaged spectrograms, STSP is able to preserve most of the speaker information in the feature maps using a few spectral components only. We show that statistics pooling is a special case of STSP where only the DC spectral components are used. Experiments on VoxCeleb1 and VOiCES 2019 show that STSP outperforms statistics pooling and multi-head attentive pooling, which suggests that leveraging more spectral information in the CNN feature maps can produce highly discriminative speaker embeddings.

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5261-Tu_Mak_STSP-icassp21.pdf

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