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In order to detect Chinese spelling errors, especially for essays written by foreign learners, a word vector/conditional random field (CRF)based detector is proposed in this paper. The main idea is to project each word in a test sentence into a high dimensional vector space in order to reveal and examine their relationships by using a CRF. The results are then utilized to constrain the time-consuming language model rescoring procedure. Official SIGHAN-2015 evaluation results show that our system did achieve reasonable performance with about 0.601/0.564 accuracies and 0.457/0.375 F1 scores in the detection/correction levels.

A hierarchical recurrent neural network (HRNN) for speech recognition is presented. The HRNN is trained by a generalized probabilistic descent (GPD) algorithm. Consequently, the difficulty of empirically selecting an appropriate target function for training RNNs can be avoided. Results obtained in this study indicate the proposed HRNN has the advantages of being capable of absorbing the temporal variation of speech patterns as well as possessing effective discrimination capabilities. The scaling problem of RNNs is also greatly reduced. Additionally, a realization of the system using initial/final sub-syllable models for isolated Mandarin syllable recognition is also undertaken for verifying its effectiveness. The effectiveness of the proposed HRNN is confirmed by the experimental results. Speech recognition Hierarchical Recurrent neural networks Generalized probabilistic descentDiscriminative training

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