On the joint use of noise reduction and MLLR adaptation for in-car hands-free speech recognition

This paper refers to an activity under way at the speech recognition technology level for the development of a hands-free dialogue interaction system in the car environment. The work here presented concerns the use of two noise reduction techniques, as well as MLLR adaptation, for recognition error reduction in low and medium complexity tasks, namely connected digits and spelling with or without bigram/trigram statistical constraints. Experiments are based on the use of SpeechDat Car database, a corpus collected under real noisy conditions. Results show the additive improvements in performance, obtained by adopting noise reduction techniques and MLLR adaptation