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A platform for research: civil engineering, architecture and urbanism
Speech recognition with matrix-MCE based two-dimension-cepstrum in cars
This study proposes matrix-MCE (MMCE) to reduce the influence of noises. Background noises usually degrade the performance of speech recognition. MMCE can efficiently minimize the classification error of two-dimension-cepstrum (TDC). Then the template matching employs the Gaussian-mixture-model (GMM). To evaluate the performance, the speech data used for our experiments are a set of isolated Mandarin digits. Experimental results indicate that MMCE-based TDC is very robust in the noisy environments.
Speech recognition with matrix-MCE based two-dimension-cepstrum in cars
This study proposes matrix-MCE (MMCE) to reduce the influence of noises. Background noises usually degrade the performance of speech recognition. MMCE can efficiently minimize the classification error of two-dimension-cepstrum (TDC). Then the template matching employs the Gaussian-mixture-model (GMM). To evaluate the performance, the speech data used for our experiments are a set of isolated Mandarin digits. Experimental results indicate that MMCE-based TDC is very robust in the noisy environments.
Speech recognition with matrix-MCE based two-dimension-cepstrum in cars
Gin-Der Wu, (author) / Zhen-Wei Zhu, (author)
2012-11-01
281515 byte
Conference paper
Electronic Resource
English
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