Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a word graph based feature enhancement method for robust speech recognition in noise. The approach uses signal processing based speech enhancement as a starting point, and then performs Wiener filtering to remove residual noise. During the process, a decoded word graph is used to directly guide the feature enhancement with respect to the HMM for recognition, so that the enhanced feature can match the clean speech model better in the acoustic space. The proposed word graph based feature enhancement method was tested on the Aurora 2 database. Experimental results show that an improved recognition performance can be obtained comparing with conventional signal processing based and GMM based feature enhancement methods. With signal processing based ighted noise estimation and GMM based method, the relative error rate reductions are 35.44% and 42.58%, respectively. The proposed word graph based method improves the performance further, and a relative error rate reduction of 57.89% is obtained.
['Zhi-Jie Yan', 'Frank K. Soong', 'Ren-Hua Wang']
I am writing a research paper on a idea Word Graph Based Feature Enhancement for Noisy Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Word Graph Based Feature Enhancement for Noisy Speech Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a word graph based feature enhancement method for robust speech recognition in noise. The approach uses signal processing based speech enhancement as a starting point, and then performs Wiener filtering to remove residual noise. During the process, a decoded word graph is used to directly guide the feature enhancement with respect to the HMM for recognition, so that the enhanced feature can match the clean speech model better in the acoustic space. The proposed word graph based feature enhancement method was tested on the Aurora 2 database. Experimental results show that an improved recognition performance can be obtained comparing with conventional signal processing based and GMM based feature enhancement methods. With signal processing based ighted noise estimation and GMM based method, the relative error rate reductions are 35.44% and 42.58%, respectively. The proposed word graph based method improves the performance further, and a relative error rate reduction of 57.89% is obtained."}]