Multilingual query by example spoken term detection for under-resourced languages

We propose a query-by-example approach to multilingual Spoken Term Detection for under-resourced languages based on Automatic Speech Recognition. The approach overcomes the main difficulties met under these conditions, i.e., providing a new method for building multilingual acoustic models with few annotated data and searching in approximate Automatic Speech Recognition transcriptions providing high scalability. The acoustic models are obtained by adapting well-trained phonemes to the ones from the envisaged languages. The mapping is made according to International Phonetic Alphabet phoneme classification and a confusion matrix. The weighting of query length and alignment spread are incorporated in the Dynamic Time Warping technique to improve the searching method. Experimental validation was conducted on a standard data set consisting of 3 hours of mixed African languages. The recorded speech has telephonic quality and it is a mix of read and spontaneous speech.