Improvement of a speech recognizer for standardized medical assessment of children's speech by integration of prior knowledge

Speech recognition of children is a more difficult task than speech recognition of adults. This problem is amplified for children with articulation disorders like cleft lip and palate (CLP). In this work we improved our automatic speech recognition system by integrating prior knowledge. Prior knowledge focuses on two different aspects: A test-dependent language modeling and an age-dependent acoustic modeling. These two approaches are merged at the end to different test- and age-dependent recognizers. We evaluated our system on a dataset of 35 children with CLP. Significant improvements could be found on this dataset. With our baseline system we achieved a negative word accuarcy (WA) of −11.0%. By an extended language modeling we achieved 27.5%. The age-dependent recognition system gains a huge improvement and achieves aWA of 42.6%. With the significant improvements in WA it is possible to perform an automatic detection and identification of specific words. Thus, we took the first step towards a speech assessment on word and subword level.