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In recent years, the i-vector based framework has been proven to provide state-of-the-art performance in the speaker verification field. Each utterance is projected onto a total factor space and is represented by a low-dimensional i-vector. However, the degradation of performance in the i-vector space remains problematic and is commonly attributed to channel variability. Most techniques used for the...
Recently deep learning has been successfully used in speech recognition, however it has not been carefully explored and widely accepted for speaker verification. To incorporate deep learning into speaker verification, this paper proposes novel approaches of extracting and using features from deep learning models for text-dependent speaker verification. In contrast to the traditional short-term spectral...
Over recent years, i-vector-based framework has been proven to provide state-of-the-art performance in speaker verification. Each utterance is projected onto a total factor space and is represented by a low-dimensional feature vector. Channel compensation techniques are carried out in this low-dimensional feature space. Most of the compensation techniques take the sets of extracted i-vectors as input...
Due to great success of deep learning in speech recognition, there has been interest of applying deep learning to speaker verification. Previous investigations usually focus on using deep neural network as new classifiers or to extract speaker dependent features. They are either not compatible with existing speaker verification approaches, or not able to achieve significant performance gain in large...
Context-dependent deep neural network (CD-DNN) has been successfully used in large vocabulary continuous speech recognition (LVCSR). However the immense computational cost of the mini-batch based back-propagation (BP) training has become a major block to utilize massive speech data for DNN training. Previous works on BP training acceleration mainly focus on parallelization with multiple GPUs. In this...
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