Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider the problem of designing a linear transformation θ ∈ Rp × n, of rank p ≤ n, which projects the features of a classifier x ∈ Rn onto y = θx ∈ Rp such as to achieve minimum Bayes error (or probability of misclassification). Two avenues will be explored: the first is to maximize the θ-average divergence beten the class densities and the second is to minimize the union Bhattacharyya bound in the range of θ. While both approaches yield similar performance in practice, they outperform standard LDA features and show a 10% relative improvement in the word error rate over state-of-the-art cepstral features on a large vocabulary telephony speech recognition task.
['George Saon', 'Mukund Padmanabhan']
I am writing a research paper on a idea Minimum Bayes Error Feature Selection for Continuous Speech Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Minimum Bayes Error Feature Selection for Continuous 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:  consider the problem of designing a linear transformation \u03b8 \u2208 Rp \u00d7 n, of rank p \u2264 n, which projects the features of a classifier x \u2208 Rn onto y = \u03b8x \u2208 Rp such as to achieve minimum Bayes error (or probability of misclassification). Two avenues will be explored: the first is to maximize the \u03b8-average divergence beten the class densities and the second is to minimize the union Bhattacharyya bound in the range of \u03b8. While both approaches yield similar performance in practice, they outperform standard LDA features and show a 10% relative improvement in the word error rate over state-of-the-art cepstral features on a large vocabulary telephony speech recognition task."}]