Thomas Fang Zheng

Learn More
The performance of the Mel-Frequency Cepstrum Coefficients (MFCC) may be affected by (1) the number of filters, (2) the shape of filters, (3) the way in which filters are spaced, and (4) the way in which the power spectrum is warped. In this paper, several comparison experiments are done to find a best implementation. The traditional MFCC calculation(More)
Emotion is one of the important factors that cause the system performance degradation. By analyzing the similarity between channel effect and emotion effect on speaker recognition, an emotion compensation method called emotion attribute projection (EAP) is proposed to alleviate the intraspeaker emotion variability. The use of this method has achieved an(More)
This paper describes the new framework of context-dependent (CD) Initial/Final (IF) acoustic modeling using the decision tree based state tying for continuous Chinese speech recognition. The Extended Initial/Final (XIF) set is chosen as the basic speech recognition unit (SRU) set according to the Chinese language characteristics, which outperforms the(More)
Besides background noise, channel effect and speaker’s health condition, emotion is another factor which may influence the performance of a speaker verification system. In this paper, the performance of a GMM-UBM based speaker verification system on emotional speech is studied. It is found that speech with various emotions aggravates the verification(More)
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing(More)
Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning(More)
Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises.(More)
Understanding intent underlying search query recently attracted enormous research interests. Two challenging issues are worth noting: First, words within query are usually ambiguous while query in most cases is too short to disambiguate. Second, ambiguity in some cases cannot be resolved according merely to the limited query context. It is thus demanded(More)
Short Utterance Speaker Recognition (SUSR) is an important area of speaker recognition when only small amount of speech data is available for testing and training. We list the most commonly used state-of-the-art methods of speaker recognition and the significance of prosodic speaker recognition. A short survey of SUSR is hereby conducted, highlighting(More)
In this paper, we propose an approach of multi-layered feature combination associated with support vector machine (SVM) for Chinese accent identification. The multi-layered features include both segmental and suprasegmental information, such as MFCC and pitch contour, to capture the diversity of variations in Chinese accented speech. The pitch contour is(More)