Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  examine a coding scheme for quantising feature vectors in a distributed speech recognition environment that is more robust to noise. It consists of a vector quantiser that operates on the logarithmic filterbank energies (LFBEs). Through the use of a perceptually-ighted Euclidean distance measure, which emphasises the LFBEs that represent the spectral peaks, the vector quantiser codebook provides ap rioriknowledge of the spectral characteristics of clean speech and is used to quantise features from noise-corrupted speech. Our comparative results from the ETSI Aurora-2 recognition task show that the perceptually-ighted vector quantisation of LFBEs achieves higher recognition accuracies for noisy speech than the unighted vector quantisation, memoryless and multi-frame GMM-based block quantisation and scalar quantisation of Mel frequency-warped cepstral coefficients.
['Stephen So', 'Kuldip Kumar Paliwal']
I am writing a research paper on a idea Improved Noise-Robustness in Distributed Speech Recognition via Perceptually-Weighted Vector Quantisation of Filterbank Energies
[{"from": "human", "value": "I am writing a research paper on a idea Improved Noise-Robustness in Distributed Speech Recognition via Perceptually-Weighted Vector Quantisation of Filterbank Energies"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  examine a coding scheme for quantising feature vectors in a distributed speech recognition environment that is more robust to noise. It consists of a vector quantiser that operates on the logarithmic filterbank energies (LFBEs). Through the use of a perceptually-ighted Euclidean distance measure, which emphasises the LFBEs that represent the spectral peaks, the vector quantiser codebook provides ap rioriknowledge of the spectral characteristics of clean speech and is used to quantise features from noise-corrupted speech. Our comparative results from the ETSI Aurora-2 recognition task show that the perceptually-ighted vector quantisation of LFBEs achieves higher recognition accuracies for noisy speech than the unighted vector quantisation, memoryless and multi-frame GMM-based block quantisation and scalar quantisation of Mel frequency-warped cepstral coefficients."}]