Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a multi-speaker articulatory reconstruction method based on speaker-independent articulatory features and speaker-dependent features. These features are separated by using a multi-speaker articulatory database. This separation method consists of normalizing palate positions among the speakers and separating multi-speaker articulatory data into a speaker-independent eigen articulatory HMM and a speaker-adaptive matrix by using speaker-adaptive training (SAT). With the proposed method, the average RMS errors of the measured and reconstructed articulatory parameters re 1.35 mm. This result shows that the proposed method makes it possible to control speaker idiosyncrasies in the articulatory parameter domain.
['Sadao Hiroya', 'Takemi Mochida']
I am writing a research paper on a idea Multi-speaker articulatory reconstruction based on an eigen articulatory HMM
[{"from": "human", "value": "I am writing a research paper on a idea Multi-speaker articulatory reconstruction based on an eigen articulatory HMM"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a multi-speaker articulatory reconstruction method based on speaker-independent articulatory features and speaker-dependent features. These features are separated by using a multi-speaker articulatory database. This separation method consists of normalizing palate positions among the speakers and separating multi-speaker articulatory data into a speaker-independent eigen articulatory HMM and a speaker-adaptive matrix by using speaker-adaptive training (SAT). With the proposed method, the average RMS errors of the measured and reconstructed articulatory parameters re 1.35 mm. This result shows that the proposed method makes it possible to control speaker idiosyncrasies in the articulatory parameter domain."}]