Text Dependent Speaker Verification with a Hybrid HMM/ANN System
نویسندگان
چکیده
The aim of this report was to implement a text-dependent speaker verification system using speaker adapted neural networks and to evaluate the system. The idea was to use a hybrid HMM/ANN approach, i.e. Artificial Neural Networks were used to estimate Hidden Markov Model emission posterior probabilities from speech data, and the system was implemented in C++ as a module for GIVES. The report also contains an overview over speaker verification. Methods and algorithms for network training and adaptation are explained, and the performance of the system is tested. Both Multi-Layer perceptrons and Single-Layer perceptrons are tested and compared to other speaker verification systems. The test results show that the hybrid HMM/ANN system does not perform as well as other speaker verification systems, but if the system parameters are optimised further performance might increase. Along with an analysis and summary of the project possible improvements of the system are suggested. 4 4 Acknowledgements
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