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Connectionist Temporal Classification (CTC) model has achieved state-of-the-art LVCSR performance. However, due to the introduction of the blank symbol, word-level confidence measures (CM) based on CTC model can not be easily calculated by directly using the traditional phone posterior normalization or confusion network (CN) approaches. Recently, a phone synchronous decoding (PSD) framework has been...
Connectionist temporal classification (CTC) has recently shown improved performance and efficiency in automatic speech recognition. One popular decoding implementation is to use a CTC model to predict the phone posteriors at each frame and then perform Viterbi beam search on a modified WFST network. This is still within the traditional frame synchronous decoding framework. In this paper, the peaky...
Model based VAD approaches have been widely used and achieved success in practice. These approaches usually cast VAD as a frame-level classification problem and employ statistical classifiers, such as Gaussian Mixture Model (GMM) or Deep Neural Network (DNN) to assign a speech/silence label for each frame. Due to the frame independent assumption classification, the VAD results tend to be fragile....
Dots-and-Boxes is a well-known paper-and-pencil, game for two players. It reaches a high level of complexity, posing an interesting challenge for AI development. Previous, board representation techniques for Dots-and-Boxes rely on data, structures like arrays or linked lists to facilitate operations on the, board. These representation techniques usually lack for the ability, to incrementally update...
Although context-dependent DNN-HMM systems have achieved significant improvements over GMM-HMM systems, there still exists big performance degradation if the acoustic condition of the test data mismatches that of the training data. Hence, adaptation and adaptive training of DNN are of great research interest. Previous works mainly focus on adapting the parameters of a single DNN by regularized or...
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