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-of-the-art two-stage methods.
|Number of pages
|Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
|Published - 2022
|23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
Duration: 18 Sep 2022 → 22 Sep 2022
Bibliographical notePublisher Copyright:
Copyright © 2022 ISCA.
- Unsupervised speech processing
- language acquisition
- unsupervised clustering
- unsupervised segmentation