Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a new method to grade non-native spoken language tests automatically. Traditional automated response grading approaches use manually engineered time-aggregated features (such as mean length of pauses).  propose to incorporate general time-sequence features (such as pitch) which preserve more information than time-aggregated features and do not require human effort to design.  use a type of recurrent neural network to jointly optimize the learning of high level abstractions from time-sequence features with the time-aggregated features.  first automatically learn high level abstractions from time-sequence features with a Bidirectional Long Short Term Memory (BLSTM) and then combine the high level abstractions with time-aggregated features in a Multilayer Perceptron (MLP)/Linear Regression (LR).  optimize the BLSTM and the MLP/LR jointly.  find such models reach the best performance in terms of correlation with human raters.  also find that when there are limited time-aggregated features available, our model that incorporates time-sequence features improves performance drastically.
['Zhou Yu', 'Vikram Ramanarayanan', 'David Suendermann-Oeft', 'Xinhao Wang', 'Klaus Zechner', 'Lei Chen', 'Jidong Tao', 'Aliaksei Ivanou', 'Yao Qian']
I am writing a research paper on a idea Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech
[{"from": "human", "value": "I am writing a research paper on a idea Using bidirectional lstm recurrent neural networks to learn high-level abstractions of sequential features for automated scoring of non-native spontaneous speech"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduce a new method to grade non-native spoken language tests automatically. Traditional automated response grading approaches use manually engineered time-aggregated features (such as mean length of pauses).  propose to incorporate general time-sequence features (such as pitch) which preserve more information than time-aggregated features and do not require human effort to design.  use a type of recurrent neural network to jointly optimize the learning of high level abstractions from time-sequence features with the time-aggregated features.  first automatically learn high level abstractions from time-sequence features with a Bidirectional Long Short Term Memory (BLSTM) and then combine the high level abstractions with time-aggregated features in a Multilayer Perceptron (MLP)/Linear Regression (LR).  optimize the BLSTM and the MLP/LR jointly.  find such models reach the best performance in terms of correlation with human raters.  also find that when there are limited time-aggregated features available, our model that incorporates time-sequence features improves performance drastically."}]