Unsupervised Word Segmentation using K Nearest Neighbors

Tzeviya Fuchs, Yedid Hoshen, Yossi Keshet

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


In this paper, we propose an unsupervised kNN-based approach
for word segmentation in speech utterances. Our method relies
on self-supervised pre-trained speech representations, and compares each audio segment of a given utterance to its k nearest
neighbors within the training set. Our main assumption is that
a segment containing more than one word would occur less often than a segment containing a single word. Our method does
not require phoneme discovery and is able to operate directly
on pre-trained audio representations. This is in contrast to current methods that use a two-stage approach; first detecting the
phonemes in the utterance and then detecting word-boundaries
according to statistics calculated on phoneme patterns. Experiments on two datasets demonstrate improved results over previous single-stage methods and competitive results on state-ofthe-art two-stage methods.
Original languageEnglish
Title of host publicationProc. Interspeech 2022
Number of pages5
StatePublished - 2022


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