Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays state-of-the-art speaker recognition systems obtain quite accurate results for both text-independent and text-dependent tasks as long as they are trained on a fair amount of development data from the target domain, and as long as the target data is clean. In this work  investigate the use of matched filters for speaker recognition in the framework of a small in-domain development data.  show how a matched filter can be optimized to maximize SNR (signal to noise ratio) when the noise component includes both intra-speaker variability and center/mean hyper-parameter variability. The proposed method generalizes our previous method named score stabilization and obtains significant speaker recognition error reductions.
['Hagai Aronowitz']
I am writing a research paper on a idea Speaker recognition using matched filters
[{"from": "human", "value": "I am writing a research paper on a idea Speaker recognition using matched filters"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays state-of-the-art speaker recognition systems obtain quite accurate results for both text-independent and text-dependent tasks as long as they are trained on a fair amount of development data from the target domain, and as long as the target data is clean. In this work  investigate the use of matched filters for speaker recognition in the framework of a small in-domain development data.  show how a matched filter can be optimized to maximize SNR (signal to noise ratio) when the noise component includes both intra-speaker variability and center/mean hyper-parameter variability. The proposed method generalizes our previous method named score stabilization and obtains significant speaker recognition error reductions."}]