Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multichannel ASR systems commonly use separate modules to perform speech enhancement and acoustic modeling. ,  present an algorithm to do multichannel enhancement jointly with the acoustic model, using a raw waveform convolutional LSTM deep neural network (CLDNN).  will show that our proposed method offers ∼5% relative improvement in WER over a log-mel CLDNN trained on multiple channels. Analysis shows that the proposed network learns to be robust to varying angles of arrival for the target speaker, and performs as ll as a model that is given oracle knowledge of the true location. Finally,  show that training such a network on inputs captured using multiple (linear) array configurations results in a model that is robust to a range of microphone spacings.
['Tara N. Sainath', 'Ron J. Weiss', 'Kevin W. Wilson', 'Arun Narayanan', 'Michiel Bacchiani', 'Andrew']
I am writing a research paper on a idea Speaker location and microphone spacing invariant acoustic modeling from raw multichannel waveforms
[{"from": "human", "value": "I am writing a research paper on a idea Speaker location and microphone spacing invariant acoustic modeling from raw multichannel waveforms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Multichannel ASR systems commonly use separate modules to perform speech enhancement and acoustic modeling. ,  present an algorithm to do multichannel enhancement jointly with the acoustic model, using a raw waveform convolutional LSTM deep neural network (CLDNN).  will show that our proposed method offers \u223c5% relative improvement in WER over a log-mel CLDNN trained on multiple channels. Analysis shows that the proposed network learns to be robust to varying angles of arrival for the target speaker, and performs as ll as a model that is given oracle knowledge of the true location. Finally,  show that training such a network on inputs captured using multiple (linear) array configurations results in a model that is robust to a range of microphone spacings."}]