Sequence summarizing neural networks for speaker recognition
Project Period: 1. 7. 2016 - 30. 6. 2019
Project Type: grant
Code: 5SA15094, 665860
Agency: South Moravian Region
Program: Horizon 2020
Speaker recognition, Neural networks
The proposed project deals with speaker recognition and is motivated by the huge performance gains that, in recent years, have been brought to other recognition tasks by so called neural networks (NN)s. The objective of the proposal is to develop a new type of NN that is suitable for speaker recognition and take it to the state where it is ready for practical use. So far, attempts to take advantage of NNs in speaker recognition have replaced one or more components in the state-of-the-art speaker recognition chain with NN equivalencies. However, this approach has the same limitations as the state-of-art processing chain in terms of what kind of patterns in the speech signals that be can modeled. Instead, our proposed project aims at replacing the whole speaker recognition chain with one NN that process whole utterances in one step. This approach should take better advantage of NNs ability to model complex patterns in the speech signals. The objectives of the proposal will be achieved by theoretical work (derivation of NN structure, training criteria etc.), implementation (parallelization, scalability etc.) and careful testing on real speech data (finding appropriate default settings etc.).