Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In previous work  have shown that an ASR system consisting of a dual-input DBN which simultaneously observes MFCC acoustic features and predicted phone labels that are generated by an exemplar-based Sparse Classification (SC) system can achieve better word recognition accuracies in noise than a system observing only one of those input streams.  explores two modifications of the SC input to further improve the noise robustness of the dual-input DBN system: 1) integrating more time context and 2) using N best states. Experiments on AURORA-2 reveal that the first approach significantly improves the recognition results at almost all SNRs, but particularly in the more noisy conditions, achieving up to 6.1% (absolute) accuracy gain at SNR −5 dB. The second modification shows that there is an optimal N which allows the maximum attainable accuracy to be even further improved with another 11.8% at −5 dB.
['Yang Sun', 'Jort F. Gemmeke', 'Bert Cranen', 'Louis ten Bosch', 'Lou Boves']
I am writing a research paper on a idea Early fusion of Sparse Classification and GMM for noise robust ASR
[{"from": "human", "value": "I am writing a research paper on a idea Early fusion of Sparse Classification and GMM for noise robust ASR"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In previous work  have shown that an ASR system consisting of a dual-input DBN which simultaneously observes MFCC acoustic features and predicted phone labels that are generated by an exemplar-based Sparse Classification (SC) system can achieve better word recognition accuracies in noise than a system observing only one of those input streams.  explores two modifications of the SC input to further improve the noise robustness of the dual-input DBN system: 1) integrating more time context and 2) using N best states. Experiments on AURORA-2 reveal that the first approach significantly improves the recognition results at almost all SNRs, but particularly in the more noisy conditions, achieving up to 6.1% (absolute) accuracy gain at SNR \u22125 dB. The second modification shows that there is an optimal N which allows the maximum attainable accuracy to be even further improved with another 11.8% at \u22125 dB."}]