The ArtiPhon Task at Evalita 2016
Leonardo Badino · Accademia University Press eBooks · 2016
Despite the impressive results achieved by ASR technology in the last few years, state-of-the-art ASR systems can still perform poorly when training and testing conditions are different (e.g., different acoustic environments). This is usually referred to as the mismatch problem. In the ArtiPhon task at Evalita 2016 we wanted to evaluate phone recognition systems in mismatched speaking styles. While training data consisted of read speech, most of testing data consisted of single-speaker hypo- and hyper-articulated speech. A second goal of the task was to investigate whether the use of speech production knowledge, in the form of measured articulatory movements, could help in building ASR systems that are more robust to the effects of the mismatch problem. Here I report the result of the only entry of the task and of baseline systems.