{'fraction_non_alphanumeric': 0.023029229406554472, 'fraction_numerical': 0.002657218777679362, 'mean_word_length': 4.978835978835979, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improved mandarin spoken term detection by using deep neural network for keyword verification

In this paper, we propose to use Deep Neural Network (DNN), which has been proved to be the state-of-the-art technique in speech recognition, to re-estimate the confidence of keyword hypotheses in the verification stage of spoken term detection. The speech recognition system based on DNN outperforms that based on conventional Gaussian Mixture Model (GMM) but suffers from the increased decoding time. When the speed of decoding or indexing is critical, it seems to be a trade-off between the performance and the speed to utilize DNN in keyword verification. Inspired by the utilization and acceleration of DNN in the decoding stage, we explored an efficient method to replace GMM by DNN in the verification stage. 5% relative reduction of equal error rate (EER) is achieved and the improvement of recall in the high precision region is especially significant, which is essential to practical tasks. Meanwhile, the search time decreases more than 50% compared to the time derived from the verification on DNN without any refinements.