Robust automatic speech recognition for children

2014-10-31Developing a robust ASR system for children is a challenging task because of increased variability in acoustic and linguistic correlates as function of young age. The acoustic variability is mainly due to the developmental changes associated with vocal tract growth. On the linguistic side, the variability is associated with limited knowledge of vocabulary, pronunciations and other linguistic constructs. ❧ This study aims to develop a more robust acoustic modeling framework through a better front-end speech processing, pronunciation modeling, duration modeling, subword modeling for more accurate children's speech recognition. The thesis study focuses on the comprehensive tests of pre-existing methods in acoustic modeling for children's speech recognition. The results are presented as a function of age to study the effect of age on the performance. The inter-database effects are taken into account by using multiple databases of children's speech. Pronunciation modeling is introduced for children's speech. The results show promising performance improvements over the baseline. To capture pronunciation mistakes and stammering specific to children, a modified version of a pre-existing subword modeling is used. The novel contribution to the subword modeling lies in FSM based subword based decoding. Motivated by the increased temporal variability found in children, duration modeling is applied for children's speech and its effectiveness is presented