Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an automatic stream-ight optimization method for noise-robust speaker verification using multi-stream HMMs integrating spectral and prosodic information. The paper first shows the effectiveness of the multi-stream technique in our speaker verification framework. Next, a stream-ight adaptation method combining the linear discriminant analysis (LDA) and Adaboost techniques is proposed. Experiments re conducted using four-connected-digit utterances of Japanese contaminated by white noise with various SNRs. Experimental results show that 1) the verification performance was improved in all SNR conditions by using stream ights estimated by the LDA and 2) the performance is further improved by using the Adaboost in 10 - 30dB SNR conditions.
['Taichi Asami', 'Koji Iwano', 'Sadaoki Furui']
I am writing a research paper on a idea Stream-Weight Optimization by LDA and Adaboost for Multi-Stream Speaker Verification
[{"from": "human", "value": "I am writing a research paper on a idea Stream-Weight Optimization by LDA and Adaboost for Multi-Stream Speaker Verification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes an automatic stream-ight optimization method for noise-robust speaker verification using multi-stream HMMs integrating spectral and prosodic information. The paper first shows the effectiveness of the multi-stream technique in our speaker verification framework. Next, a stream-ight adaptation method combining the linear discriminant analysis (LDA) and Adaboost techniques is proposed. Experiments re conducted using four-connected-digit utterances of Japanese contaminated by white noise with various SNRs. Experimental results show that 1) the verification performance was improved in all SNR conditions by using stream ights estimated by the LDA and 2) the performance is further improved by using the Adaboost in 10 - 30dB SNR conditions."}]