Query-by-Example Spoken Term Detection using low dimensional posteriorgrams motivated by articulatory classes

This paper addresses the problem of Query-by-Example Spoken Term Detection (QbE-STD). Posteriorgrams have been widely used in the research on QbE-STD. Features based on articulatory classes are known to be robust to phonemic variations. The articulatory features like voicing and place of articulation are the main distinguishing features among some plosives and fricatives. These properties inspire the study of posteriorgrams based on articulatory classes for QbE-STD. Most of the previous works based on articulatory features have defined a large number of articulatory classes making it difficult to use them directly for pattern matching. Also, most of the works have completely ignored the uniqueness of the phonemes having transitory places of articulation eg. diphthongs and approximants. These issues have been addressed in this work while carefully selecting low dimensional articulatory motivated (LDAM) posteriorgrams on the basis of detailed experiments. This work is the first to show that the articulatory based posteriorgrams can outperform the phonemic posteriorgram significantly in a stand alone way (without any support from acoustic or phonemic features) for the task of QbE-STD.