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- W2250673721 abstract "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." @default.
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- W2250673721 date "2015-01-01" @default.
- W2250673721 modified "2023-09-24" @default.
- W2250673721 title "Automated Speech Recognition Technology for Dialogue Interaction with Non-Native Interlocutors" @default.
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- W2250673721 doi "https://doi.org/10.18653/v1/w15-4617" @default.
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