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A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic discriminate/speaker discriminate DNN as a feature extractor for speaker verification has shown promising results. The extracted frame-level (bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation...
Recurrent Neural Networks (RNNs) using Long-Short Term Memory (LSTM) architecture have demonstrated the state-of-the-art performances on speech recognition. Most of deep RNNs use the softmax activation function in the last layer for classification. This paper illustrates small but consistent advantages of replacing the softmax layer in RNN with Support Vector Machines (SVMs). The parameters of RNNs...
Deep learning is becoming a mainstream technology for speech recognition at industrial scale. In this paper, we provide an overview of the work by Microsoft speech researchers since 2009 in this area, focusing on more recent advances which shed light to the basic capabilities and limitations of the current deep learning technology. We organize this overview along the feature-domain and model-domain...
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