Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To produce speech synchronized articulatory animation, Electro-Magnetic Articulography (EMA) data is one type of important training data for establishing the relationship beten speech and articulatory movements. Because the EMA data is easily contaminated by the head motion during the capturing process, this paper proposes a real-time robust stabilization system for EMA noisy data. Firstly, global motion parameters are obtained by fitting the EMA noisy data beten the reference frame and current frame with random sample consensus algorithm. Secondly, multiple evaluation criteria, i.e., global motion parameters and location errors of corresponding EMA noisy data matches, are fused by an adaptive low-pass filter to smooth global motion for obtaining correction vector. Finally, motion compensation is applied to the current frame by using correction vector, and stabilized EMA data is obtained. By comparing beten the EMA noisy data and stabilized EMA data, the experimental results demonstrate the system can increase the average peak signal-to-noise ratio around 5.92 dB, the perceptive comfort on the speech synchronized articulatory animation driven by the stabilized EMA data.
['Jun Yu', 'Chen Jiang', 'Changwei Luo', 'Rui Li', 'Lingyan Li', 'Zengfu Wang']
I am writing a research paper on a idea Electro-Magnetic Articulography data stabilization for speech synchronized articulatory animation
[{"from": "human", "value": "I am writing a research paper on a idea Electro-Magnetic Articulography data stabilization for speech synchronized articulatory animation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: To produce speech synchronized articulatory animation, Electro-Magnetic Articulography (EMA) data is one type of important training data for establishing the relationship beten speech and articulatory movements. Because the EMA data is easily contaminated by the head motion during the capturing process, this paper proposes a real-time robust stabilization system for EMA noisy data. Firstly, global motion parameters are obtained by fitting the EMA noisy data beten the reference frame and current frame with random sample consensus algorithm. Secondly, multiple evaluation criteria, i.e., global motion parameters and location errors of corresponding EMA noisy data matches, are fused by an adaptive low-pass filter to smooth global motion for obtaining correction vector. Finally, motion compensation is applied to the current frame by using correction vector, and stabilized EMA data is obtained. By comparing beten the EMA noisy data and stabilized EMA data, the experimental results demonstrate the system can increase the average peak signal-to-noise ratio around 5.92 dB, the perceptive comfort on the speech synchronized articulatory animation driven by the stabilized EMA data."}]