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Deep Neural Networks (DNN) are the dominant technique widely used in English and Chinese speech recognition currently. However, Tibetan speech recognition research starts late and mainly uses Hidden Markov Model (HMM). In this paper, We show a better method of replacing Gaussian Mixture Models (GMM) by DNN to Tibetan Lhasa dialect speech recognition system. The system contains seven layers of features...
This paper proposes a novel regularized adaptation method for long short term memory (LSTM) recurrent neural network (RNN) based acoustic model trained with connectionist temporal classification (CTC) loss function (LSTM-RNN-CTC) to improve the performance of multi-accent Mandarin speech recognition task. In general, directly adjusting the network parameters with a small adaptation set may lead to...
The paper introduces theory of spatial scales as well as the processes of extracting planar SIFT characters in details. Meanwhile, aiming at defects of SIFT features, the paper proposes an improved SIFT characters. Firstly, extract SIFT characters for all frame images in the shot, and then screen them through the thought of spatio-temporal interest point, the process of which can separate parts of...
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