Combining Vocal Tract Length Normalization With Hierarchical Linear Transformations
This paper describes experiments in creating personalised children's voices for HMM-based synthesis by adapting either an adult or child average voice. The adult average voice is trained from a large adult speech database, whereas the child average voice is trained using a small database of children's speech. Here we present the idea to use stacked transformations for creating synthetic child voices, where the child average voice is first created from the adult average voice through speaker adaptation using all the pooled speech data from multiple children and then adding child specific speaker adaptation on top of it. VTLN is applied to speech synthesis to see whether it helps the speaker adaptation when only a small amount of adaptation data is available. The listening test results show that the stacked transformations significantly improve speaker adaptation for small amounts of data, but the additional benefit provided by VTLN is not yet clear.