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.

提供了一种基于无监督贝叶斯模型的语音分割以及聚类算法，通过将有潜力的单词段嵌入固定维度的声学向量空间，并在此空间创建全词声学模型以及同时执行分割来实现输入语音基于单词类型的完整无人监督标记，模型在连接数字识别任务中取得约 20% 的错误率，优于基于 HMM 的系统。