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Dual supervised learning for non-native speech recognition
2019
EURASIP Journal on Audio, Speech, and Music Processing
Current automatic speech recognition (ASR) systems achieve over 90-95% accuracy, depending on the methodology applied and datasets used. However, the level of accuracy decreases significantly when the same ASR system is used by a non-native speaker of the language to be recognized. At the same time, the volume of labeled datasets of non-native speech samples is extremely limited both in size and in the number of existing languages. This problem makes it difficult to train or build sufficiently
doi:10.1186/s13636-018-0146-4
fatcat:pnfntaxdjjcidk5t75waucffbu