Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an extension of Probabilistic Space Maps (PS-MAPS) to adapt clean acoustic models to a noisy environment. In the presence of noise, the relationship beten noisy and clean speech features (MFCC's, HDLA, etc.) is either nonlinear or unknown. Given the relationship beten features, traditional methods try to linearize it using approximations. These methods cannot be used for systems where the mapping model for clean and noisy features is missing. Given sufficient training data, PS-MAPS [1] provides an excellent framework for extracting and modeling this relationship. The PS-MAP based approach to model adaptation is completely data driven. Experiments re performed on Aurora 2 dataset to evaluate the effectiveness of the algorithm.
['Kaustubh Kalgaonkar', 'Mark A. Clements']
I am writing a research paper on a idea HMM adaptation using sparse Probabilistic Space Mapping for noisy speech
[{"from": "human", "value": "I am writing a research paper on a idea HMM adaptation using sparse Probabilistic Space Mapping for noisy speech"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an extension of Probabilistic Space Maps (PS-MAPS) to adapt clean acoustic models to a noisy environment. In the presence of noise, the relationship beten noisy and clean speech features (MFCC's, HDLA, etc.) is either nonlinear or unknown. Given the relationship beten features, traditional methods try to linearize it using approximations. These methods cannot be used for systems where the mapping model for clean and noisy features is missing. Given sufficient training data, PS-MAPS [1] provides an excellent framework for extracting and modeling this relationship. The PS-MAP based approach to model adaptation is completely data driven. Experiments re performed on Aurora 2 dataset to evaluate the effectiveness of the algorithm."}]