Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as “Sparse Auditory Reproducing Kernel” (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a reproducing kernel Hilbert space (RKHS) spanned by overcomplete, nonlinear, and time-shifted gammatone basis functions. The feature extraction algorithm first involves computing kernel based similarity beten the speech signal and the time-shifted gammatone functions, follod by feature pruning using a simple pooling technique (“MAX” operation). ,  describe the effect of different hyper-parameters and kernel functions on the performance of a SPARK based speech recognizer. Experimental results based on the standard AURORA2 dataset demonstrate that the SPARK based speech recognizer delivers consistent improvements in word-accuracy when compared with a baseline speech recognizer trained using the standard ETSI STQ WI008 DSR features.
['Amin Fazel', 'Shantanu Chakrabartty']
I am writing a research paper on a idea Sparse Auditory Reproducing Kernel (SPARK) Features for Noise-Robust Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Sparse Auditory Reproducing Kernel (SPARK) Features for Noise-Robust 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: ,  present a novel speech feature extraction algorithm based on a hierarchical combination of auditory similarity and pooling functions. The computationally efficient features known as \u201cSparse Auditory Reproducing Kernel\u201d (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a reproducing kernel Hilbert space (RKHS) spanned by overcomplete, nonlinear, and time-shifted gammatone basis functions. The feature extraction algorithm first involves computing kernel based similarity beten the speech signal and the time-shifted gammatone functions, follod by feature pruning using a simple pooling technique (\u201cMAX\u201d operation). ,  describe the effect of different hyper-parameters and kernel functions on the performance of a SPARK based speech recognizer. Experimental results based on the standard AURORA2 dataset demonstrate that the SPARK based speech recognizer delivers consistent improvements in word-accuracy when compared with a baseline speech recognizer trained using the standard ETSI STQ WI008 DSR features."}]