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This paper presents the "Ethiopian" system for the SLT 2021 Children SpeechRecognition Challenge. Various data processing and augmentation techniques areproposed to tackle children's speech recognition problem, especially the lackof the children's speech recognition training data issue. Detailed experimentsare designed and conducted to show the effectiveness of each technique, acrossdifferent speech recognition toolkits and model architectures. Step by step, weexplain how we come up with our final system, which provides thestate-of-the-art results in the SLT 2021 Children Speech Recognition Challenge,with 21.66% CER on the Track 1 evaluation set (4th place overall), and 16.53%CER on the Track 2 evaluation set (1st place overall). Post-challenge analysisshows that our system actually achieves 18.82% CER on the Track 1 evaluationset, but we submitted the wrong version to the challenge organizer for Track 1.

Data Augmentation For Children's Speech Recognition -- The "Ethiopian" System For The SLT 2021 Children Speech Recognition Challenge