Improving Children's Speech Recognition Through Out of Domain Data Augmentation
Children's speech poses challenges to speech recognition due to strong age dependent anatomical variations and a lack of large, publicly available corpora. In this paper we explore data augmentation for children's speech recognition using stochastic feature mapping (SFM) to transform out of domain adult data for both GMM based and DNN based acoustic models. We performed experiments on the English PF STAR corpus, augmenting using WSJCAM0 and ABI. Our experimental results indicate that a DNN acoustic model for childrens speech can make use of adult data, and that out of domain SFM is more accurate than in domain SFM.
INTERSPEECH