Improved keyword spotting system by optimizing posterior confidence measure vector using feed-forward neural network

In this paper, a novel method based on feedforward neural network is proposed to optimize the confidence measure for improving a mandarine keyword spotting system. Keyword spotting is to detect the occurrences of a pre-defined list of keywords in the input speech, and confidence measure is an critical part in the verification stage of keyword spotting. Posterior confidence has been widely used and was verified to be effective. In some previous works, the optimization of posterior confidence has been proposed, which linearly transforms the phone-level confidence into the word-level confidence. On this basis, we propose a neural network based method that make a non-linear transformation. In addition, a sparse activation and back-propagation strategy is proposed to make this method feasible and work fast. In the experiments, the proposed method is compared to other two previous methods. To evaluate performance, two most commonly used measures are considered: AUC and EER. The experimental result shows that the proposed method is effective and achieved the best performance among three methods.