Exploring multi-language resources for unsupervised spoken term discovery

With information processing and retrieval of spoken documents becoming an important topic, there is a need of systems performing automatic segmentation of audio streams. Among such algorithms, spoken term discovery allows the extraction of word-like units (terms) directly from the continuous speech signal, in an unsupervised manner and without any knowledge of the language at hand. Since the performance of any downstream application depends on the goodness of the terms found, it is relevant to try to obtain higher quality automatic terms. In this paper we investigate whether the use input features derived from of multi-language resources helps the process of term discovery. For this, we employ an open-source phone recognizer to extract posterior probabilities and phone segment decisions, for several languages. We examine the features obtained from a single language and from combinations of languages based on the spoken term discovery results attained on two different datasets of English and Xitsonga. Furthermore, a comparison to the results obtained with standard spectral features is performed and the implications of the work discussed.