Experiments on hiwire database using denoising and adaptation with a hybrid HMM-ANN model

This paper presents the results of a large number of experiments performed on the Hiwire cockpit database with a hybrid HMM-ANN speech recognition model. The Hiwire database is a noisy and non-native English speech corpus for cockpit communication. The noisy component of the database has been used to test two noise reduction methods recently introduced, while the adaptation component is exploited to perform supervised and unsupervised adaptation of the HMM-ANN model with an innovative technology, both in multi-speaker and speaker dependent way. Baseline results are presented, and the improvements obtained with noise reduction and adaptations are reported, showing an error reduction of about 60%.