Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  evaluate the performance of several feature sets on the Aurora task as defined by ETSI.  show that after a non-linear transformation, a number of features can be effectively used in a HMM-based recognition system. The non-linear transformation is computed using a neural network which is discriminatively trained on the phonetically labeled (forcibly aligned) training data. A combination of the non-linearly transformed PLP (perceptive linear predictive coefficients), MSG (modulation filtered spectrogram) and TRAP (temporal pattern) features yields a 63% improvement in error rate as compared to baseline me frequency cepstral coefficients features. The use of the non-linearly transformed RASTA-like features, with system parameters scaled down to take into account the ETSI imposed memory and latency constraints, still yields a 40% improvement in error rate.
['Sangita Sharma', 'Daniel P. W. Ellis', 'Sachin S. Kajarekar', 'Pratibha Jain', 'Hynek Hermansky']
I am writing a research paper on a idea Feature extraction using non-linear transformation for robust speech recognition on the Aurora database
[{"from": "human", "value": "I am writing a research paper on a idea Feature extraction using non-linear transformation for robust speech recognition on the Aurora database"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  evaluate the performance of several feature sets on the Aurora task as defined by ETSI.  show that after a non-linear transformation, a number of features can be effectively used in a HMM-based recognition system. The non-linear transformation is computed using a neural network which is discriminatively trained on the phonetically labeled (forcibly aligned) training data. A combination of the non-linearly transformed PLP (perceptive linear predictive coefficients), MSG (modulation filtered spectrogram) and TRAP (temporal pattern) features yields a 63% improvement in error rate as compared to baseline me frequency cepstral coefficients features. The use of the non-linearly transformed RASTA-like features, with system parameters scaled down to take into account the ETSI imposed memory and latency constraints, still yields a 40% improvement in error rate."}]