Unit selection synthesis based data augmentation for fixed phrase speaker verification
This addresses the high cost of collecting target-transcript data for speaker verification systems, though it is an incremental improvement on existing data augmentation techniques.
The paper tackles the problem of limited training data for text-dependent speaker verification by proposing a unit selection synthesis method that converts abundant text-independent speech into target-transcript data, achieving significant performance improvements on the AISHELL 2019 database.
Data augmentation is commonly used to help build a robust speaker verification system, especially in limited-resource case. However, conventional data augmentation methods usually focus on the diversity of acoustic environment, leaving the lexicon variation neglected. For text dependent speaker verification tasks, it's well-known that preparing training data with the target transcript is the most effectual approach to build a well-performing system, however collecting such data is time-consuming and expensive. In this work, we propose a unit selection synthesis based data augmentation method to leverage the abundant text-independent data resources. In this approach text-independent speeches of each speaker are firstly broke up to speech segments each contains one phone unit. Then segments that contain phonetics in the target transcript are selected to produce a speech with the target transcript by concatenating them in turn. Experiments are carried out on the AISHELL Speaker Verification Challenge 2019 database, the results and analysis shows that our proposed method can boost the system performance significantly.