Towards age-independent acoustic modeling

In automatic speech recognition applications, due to significant differences in voice characteristics, adults and children are usually treated as two population groups, for which different acoustic models are trained. In this paper, age-independent acoustic modeling is investigated in the context of large vocabulary speech recognition. Exploiting a small amount (9 hours) of children`s speech and a more significant amount (57 hours) of adult speech, age-independent acoustic models are trained using several methods for speaker adaptive acoustic modeling. Recognition results achieved using these models are compared with those achieved using age-dependent acoustic models for children and adults, respectively. Recognition experiments are performed on four Italian speech corpora, two consisting of children`s speech and two of adult speech, using 64k word and 11k word trigram language models. Methods for speaker adaptive acoustic modeling prove to be effective for training age-independent acoustic models ensuring recognition results at least as good as those achieved with age-dependent acoustic models for adults and children.