CONSPECTUS:The increasing demands of sustainable energy,electronics,and biomedical applications call for next-generation functional materials with unprecedented properties.Of particular interest are emerging materials...CONSPECTUS:The increasing demands of sustainable energy,electronics,and biomedical applications call for next-generation functional materials with unprecedented properties.Of particular interest are emerging materials that display exceptional physical properties,making them promising candidates for energy-efficient microelectronic devices.As the conventional Edisonian approach becomes significantly outpaced by growing societal needs,emerging computational modeling and machine learning methods have been employed for the rational design of materials.展开更多
Parkinson’s disease patients suffer from disorders of speech.The most frequently reported speech problems are weak,hoarse,nasal or monotonous voice,imprecise articulation,slow or fast speech,difficulty starting speec...Parkinson’s disease patients suffer from disorders of speech.The most frequently reported speech problems are weak,hoarse,nasal or monotonous voice,imprecise articulation,slow or fast speech,difficulty starting speech,impaired stress or rhythm,stuttering,and tremor.To improve the speech quality and assist the patient with speech rehabilitation therapy,we have proposed the speech recognition model for Parkinson’s disease patients using transfer learning technique(PSTL),where we have pre-trained the long short-term memory(LSTM)neural network model with our developed publicly available dataset that has been obtained from healthy people through the social media platform.Then,we applied the transfer learning technique to improve the performance of the PSTL framework.The frequency spectrogram masking data augmentation method has been used to alleviate the over-fitting problem so that the word error rate(WER)is further reduced.Even with a limited dataset,our proposed model has effectively reduced the WER from 58% to 44.5% on the original speech dataset and 53.1% to 43% on the denoised speech dataset,which demonstrated the feasibility of our framework.展开更多
基金the Advanced Research Projects Agency-Energy(ARPA-E),U.S.Department of Energy,under award number DE-AR0001209the National Science Foundation(NSF)Division of Materials Research,under award number DMR-2324173(H.Z.,J.M.R.,and W.C.)+1 种基金the Air Force Office of Scientific Research(AFOSR),under award number FA9550-24-1-0301(W.C.)the Ryan Graduate Fellowship(H.Z.).
文摘CONSPECTUS:The increasing demands of sustainable energy,electronics,and biomedical applications call for next-generation functional materials with unprecedented properties.Of particular interest are emerging materials that display exceptional physical properties,making them promising candidates for energy-efficient microelectronic devices.As the conventional Edisonian approach becomes significantly outpaced by growing societal needs,emerging computational modeling and machine learning methods have been employed for the rational design of materials.
基金the National Key Research and Development Program of China(No.2019YFB2204500)the Science,Technology and Innovation Action Plan of Shanghai Municipality(No.1914220370)。
文摘Parkinson’s disease patients suffer from disorders of speech.The most frequently reported speech problems are weak,hoarse,nasal or monotonous voice,imprecise articulation,slow or fast speech,difficulty starting speech,impaired stress or rhythm,stuttering,and tremor.To improve the speech quality and assist the patient with speech rehabilitation therapy,we have proposed the speech recognition model for Parkinson’s disease patients using transfer learning technique(PSTL),where we have pre-trained the long short-term memory(LSTM)neural network model with our developed publicly available dataset that has been obtained from healthy people through the social media platform.Then,we applied the transfer learning technique to improve the performance of the PSTL framework.The frequency spectrogram masking data augmentation method has been used to alleviate the over-fitting problem so that the word error rate(WER)is further reduced.Even with a limited dataset,our proposed model has effectively reduced the WER from 58% to 44.5% on the original speech dataset and 53.1% to 43% on the denoised speech dataset,which demonstrated the feasibility of our framework.
