Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the inconsistency beten the maximum likelihood (ML) based training and the synthesis application in HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed for HMM training.  continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis.  propose a MGE linear regression (MGELR) based model adaptation algorithm, where the regression matrices used to transform source models to target models are optimized to minimize the generation errors for the input speech data uttered by the target speaker. The proposed MGELR approach was compared with the maximum likelihood linear regression (MLLR) based model adaptation. Experimental results indicate that the generation errors re reduced after the MGELR-based model adaptation. And from the subjective listening test, the discrimination and the quality of the synthesized speech using MGELR re better than the results using MLLR.
['Long Qin', 'Yi-Jian Wu', 'Zhen-Hua Ling', 'Ren-Hua Wang', 'Li-Rong Dai']
I am writing a research paper on a idea Minumum generation error linear regression based model adaptation for HMM-based speech synthesis
[{"from": "human", "value": "I am writing a research paper on a idea Minumum generation error linear regression based model adaptation for HMM-based speech synthesis"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Due to the inconsistency beten the maximum likelihood (ML) based training and the synthesis application in HMM-based speech synthesis, a minimum generation error (MGE) criterion had been proposed for HMM training.  continues to apply the MGE criterion to model adaptation for HMM-based speech synthesis.  propose a MGE linear regression (MGELR) based model adaptation algorithm, where the regression matrices used to transform source models to target models are optimized to minimize the generation errors for the input speech data uttered by the target speaker. The proposed MGELR approach was compared with the maximum likelihood linear regression (MLLR) based model adaptation. Experimental results indicate that the generation errors re reduced after the MGELR-based model adaptation. And from the subjective listening test, the discrimination and the quality of the synthesized speech using MGELR re better than the results using MLLR."}]