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Very Low Resource Radio Browsing for Agile Developmental and Humanitarian Monitoring
We present a radio browsing system developed on a very small corpus of annotated speech by using semi-supervised training of multilingual DNN/HMM acoustic models. This system is intended to support relief and developmental programmes by the United Nations (UN) in parts of Africa where the spoken languages are extremely under resourced. We assume the availability of 12 minutes of annotated speech in the target language, and show how this can best be used to develop an acoustic model. First, adoi:10.21437/interspeech.2017-880 fatcat:ltdu2c2w2ja3vgsj5hsms5rfgi