Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In speaker verification, a claimant may produce two or more utterances. Typically, the scores of the speech patterns extracted from these utterances are averaged and the resulting mean score is compared with a decision threshold. Rather than simply computing the mean score,  propose to compute the optimal ights for fusing the scores based on the score distribution of the independent utterances and our prior knowledge about the score statistics. More specifically,  use enrollment data to compute the mean scores of client speakers and impostors and consider them to be the prior scores. During verification,  set the fusion ights for individual speech patterns to be a function of the dispersion beten the scores of these speech patterns and the prior scores. Experimental results based on the GSM-transcoded speech of 150 speakers from the HTIMIT corpus demonstrate that the proposed fusion algorithm can increase the dispersion beten the mean speaker scores and the mean impostor scores. Compared with a baseline approach where equal ights are assigned to all scores, the proposed approach provides a relative error reduction of 19%.
['Man-Wai Mak', 'Ming-Cheung Cheung', 'Sun-Yuan Kung']
I am writing a research paper on a idea Robust speaker verification from GSM-transcoded speech based on decision fusion and feature transformation
[{"from": "human", "value": "I am writing a research paper on a idea Robust speaker verification from GSM-transcoded speech based on decision fusion and feature transformation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In speaker verification, a claimant may produce two or more utterances. Typically, the scores of the speech patterns extracted from these utterances are averaged and the resulting mean score is compared with a decision threshold. Rather than simply computing the mean score,  propose to compute the optimal ights for fusing the scores based on the score distribution of the independent utterances and our prior knowledge about the score statistics. More specifically,  use enrollment data to compute the mean scores of client speakers and impostors and consider them to be the prior scores. During verification,  set the fusion ights for individual speech patterns to be a function of the dispersion beten the scores of these speech patterns and the prior scores. Experimental results based on the GSM-transcoded speech of 150 speakers from the HTIMIT corpus demonstrate that the proposed fusion algorithm can increase the dispersion beten the mean speaker scores and the mean impostor scores. Compared with a baseline approach where equal ights are assigned to all scores, the proposed approach provides a relative error reduction of 19%."}]