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Multilingual Data Selection for Low Resource Speech Recognition
Feature representations extracted from deep neural networkbased multilingual frontends provide significant improvements to speech recognition systems in low resource settings. To effectively train these frontends, we introduce a data selection technique that discovers language groups from an available set of training languages. This data selection method reduces the required amount of training data and training time by approximately 40%, with minimal performance degradation. We present speechdoi:10.21437/interspeech.2016-598 dblp:conf/interspeech/ThomasACKR16 fatcat:rnumqswls5ek3mfbrscx5omjwy