Gaussian segmentation and tokenization for low cost language identification

Abstract

Most common approaches to phonotactic language recognition deal with phone decoders as tokenizers. However, units that are not linked to phonetic definitions can be more universals, and therefore conceptually easier to adopt. It is assumed that the overall sound characteristics of all spoken languages can be covered by a broad collection of acoustic units, which can be characterized by acoustic segments. In this paper, such acoustic units, highly desirables for a more general language characterization, are delimited and clustered using Gaussian Mixture Model. A new segmentation method on acoustic units of the speech is proposed for later Gaussian modelling, looking for substitute the phonetic recognizer. This tokenizer is trained over untranscribed data, and it precedes the statistical language modeling phase. © Springer-Verlag 2013.