Fully Unsupervised Small-Vocabulary Speech Recognition Using a Segmental Bayesian Model

Current supervised speech technology relies heavily on tran-scribed speech and pronunciation dictionaries. In settings where unlabelled speech data alone is available, unsupervised methods are required to discover categorical linguistic structure directly from the audio. We present a novel Bayesian model which seg-ments unlabelled input speech into word-like units, resulting in a complete unsupervised transcription of the speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional space; the model (implemented as a Gibbs sampler) then builds a whole-word acoustic model in this space while jointly doing seg-mentation. We report word error rates in a connected digit recog-nition task by mapping the unsupervised output to ground truth transcriptions. Our model outperforms a previously developed HMM-based system, even when the model is not constrained to discover only the 11 word types present in the data. Index Terms: unsupervised speech processing, word discovery, speech segmentation, unsupervised learning, segmental models 1