Paper: Generating Synthetic Children’s Acoustic Models from Adult Models

ACL ID N09-2020
Title Generating Synthetic Children’s Acoustic Models from Adult Models
Venue Human Language Technologies
Session Short Paper
Year 2009
Authors

This work focuses on generating children’s HMM-based acoustic models for speech rec- ognition from adult acoustic models. Collect- ing children’s speech data is more costly compared to adult’s speech. The patent- pending method developed in this work re- quires only adult data to estimate synthetic children’s acoustic models in any language and works as follows: For a new language where only adult data is available, an adult male and an adult female model is trained. A linear transformation from each male HMM mean vector to its closest female mean vector is estimated. This transform is then scaled to a certain power and applied to the female model to obtain a synthetic children’s model. In a pronunciation verification task the method yields 19% and 3.7% relative imp...