Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels

Tzeviya Sylvia Fuchs*, Yedid Hoshen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Unsupervised word segmentation in audio utterances is challenging as, in speech, there is typically no gap between words. In a preliminary experiment, we show that recent deep self- supervised features are very effective for word segmentation but require supervision for training the classification head. To extend their effectiveness to unsupervised word segmentation, we propose a pseudo-labeling strategy. Our approach relies on the observation that the temporal gradient magnitude of the embeddings (i.e. the distance between the embeddings of subsequent frames) is typically minimal far from the boundaries and higher nearer the boundaries. We use a thresholding function on the temporal gradient magnitude to define a psuedolabel for wordness. We train a linear classifier, mapping the embedding of a single frame to the pseudo-label. Finally, we use the classifier score to predict whether a frame is a word or a boundary. In an empirical investigation, our method, despite its simplicity and fast run time, is shown to significantly outperform all previous methods on two datasets.

Original languageAmerican English
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Unsupervised speech processing
  • language acquisition
  • unsupervised segmentation

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