Unsupervised Word Acquisition from Speech using Pattern Discovery

In this paper, we present an unsupervised method for automatically discovering words from speech using a combination of acoustic pattern discovery, graph clustering, and baseform searching. The algorithm we propose represents an alternative to traditional methods of speech recognition and makes use of the acoustic similarity of multiple realizations of the same words or phrases. On a set of three academic lectures on different subjects, we show that the clustering component of the algorithm is able to successfully generate word clusters that have good coverage of subject-relevant words. Moreover, we illustrate how to use the cluster nodes to retrieve the word identity of each cluster from a large baseform dictionary. Results indicate that this algorithm may prove useful for applications such as vocabulary initialization, speech summarization, or augmentation of existing recognition systems