Aiming at the problems of effective feature extraction of speech signal and influence of noise in speaker recognition, a novel method called Mel Frequency Cepstral Coefficients based on S-transform (SMFCC) was proposed for speech feature extraction. The speech features were obtained which were based on traditional Mel Frequency Cepstral Coefficients (MFCC), employed the properties of two-dimensional Time-Frequency (TF) multiresolution in S-transform and effective denoising of two-dimensional TF matrix with Singular Value Decomposition (SVD) algorithm, and combined with other related statistic methods. Based on the TIMIT corpus, the extracted features were compared with the current features by the experiment. The Equal Error Rate (EER) and Minimum Detection Cost Function (MinDCF) of SMFCC were smaller than those of Linear Prediction Cepstral Coefficient (LPCC), MFCC, and LMFCC; especially, the EER and MinDCF08 of SMFCC were decreased by 3.6% and 17.9% respectively compared to MFCC.The experimental results show that the proposed method can eliminate the noise in the speech signal effectively and improve local speech signal feature resolution.

Concerning the low accuracy of identifying relevant Chinese events, a correlation recognition algorithm for Chinese news events based on Grey Relational Analysis (GRA) was proposed, which is a multiple factor analysis method. Firstly, three factors that affect the event correlation, including co-occurrence of triggers, shared nouns between events and the similarity of the event sentences, were proposed through analyzing the characteristics of Chinese news events. Secondly, the three factors were quantified and the influence weights of them were calculated. Finally, GRA was used to combine the three factors, and the GRA model between events was established to realize event correlation recognition. The experimental results show that the three factors for event correlation recognition are effective, and compared with the method only using one influence factor, the proposed algorithm improves the accuracy of event correlation recognition.