Two text-independent speaker recognition methods based on Selfstructuring Hidden Control (SHC) neural models and Self-structuring Pi-Sigma (SPS) neural models are proposed. We have designed the self-structuring models to achieve better model architectures i.e. data determined architectures instead of a priori determined architectures. PS and HC neural models for speaker recognition are also proposed. Each of the four methods require typically 75% less neural models compared to the predictive neural network based text-independent speaker recognition method [I] i.e. the latter contains an ergodic M-state model using M neural models (M = 4) for each speaker; each of our speaker recognition systems uses only one neural model to realize an ergodic M-state model. The Pi-Sigma models  have been modified to obtain Self-structuring PS models and our speech recognition SHC models which we presented at ICASSP92  have been changed to fit into speaker recognition systems.
|Title of host publication||ICASSP-92|
|Publication status||Published - 1993|
|Event||IEEE International Conference on Acoustics, Speech & Signal Processing - Minneapolis, USA|
Duration: 1 Jan 1993 → …
|Conference||IEEE International Conference on Acoustics, Speech & Signal Processing|
|Period||01/01/1993 → …|
Sørensen, H. B. D., & Hartmann, U. (1993). Pi-sigma and hidden control based self-structuring models for text-independent speaker recognition. In ICASSP-92 (Vol. 1, pp. 537-540). IEEE Press. https://doi.org/10.1109/ICASSP.1993.319174