Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings

Herman Kamper, Aren Jansen, Sharon Goldwater

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In settings where only unlabelled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. A similar problem is faced when modelling infant language acquisition. In these cases, categorical linguistic structure needs to be discovered directly from speech audio. We present a novel unsupervised Bayesian model that segments unlabelled speech and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation.We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute. Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.
Original languageEnglish
Pages (from-to)669-679
Number of pages11
JournalIEEE Transactions on Audio, Speech and Language Processing
Volume24
Issue number4
Early online date12 Jan 2016
DOIs
Publication statusPublished - 1 Apr 2016

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