Automated Speech Recognition Technology for Dialogue Interaction with Non-Native Interlocutors

Dialogue interaction with remote interlocutors is a difficult application area for speech recognition technology because of the limited duration of acoustic context available for adaptation, the narrow-band and compressed signal encoding used in telecommunications, high variability of spontaneous speech and the processing time constraints. It is even more difficult in the case of interacting with non-native speakers because of the broader allophonic variation, less canonical prosodic patterns, a higher rate of false starts and incomplete words, unusual word choice and smaller probability to have a grammatically well formed sentence. We present a comparative study of various approaches to speech recognition in non-native context. Comparing systems in terms of their accuracy and real-time factor we find that a Kaldi-based Deep Neural Network Acoustic Model (DNN-AM) system with online speaker adaptation by far outperforms other available methods.