Recognition of Multiple Language Voice Navigation Queries in Traffic Situations

This paper introduces our work and results related to a multiple language continuous speech recognition task. The aim was to design a system that introduces tolerable amount of recognition errors for point of interest words in voice navigational queries even in the presence of real-life traffic noise. Additional challenges were that no task-specific training databases were available for language and acoustic modeling. Instead, general purpose acoustic database were obtained and (probabilistic) context free grammars were constructed for the acoustic and language models, respectively. Public pronunciation lexicon was used for the English language, whereas ruleand exception dictionary based pronunciation modeling was applied for French, German, Italian, Spanish and Hungarian. For the last four languages the classical phoneme-based pronunciation modeling approach was compared to grapheme-based pronunciation modeling technique, as well. Noise robustness was addressed by applying various feature extraction methods. The results show that achieving high word recognition accuracy is feasible if cooperative speakers can be assumed.