Short-time spectral aggregation for speaker embedding

Youzhi Tu, Man Wai Mak

Research output: Journal article publicationConference articleAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)6708-6712
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2021-June
DOIs
Publication statusPublished - Jun 2021
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021

Keywords

  • Speaker embedding
  • Speaker verification
  • Spectral pooling
  • Statistics pooling

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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